Acknowledgement
This study has been carried out with the support of Commission staff in the
DG MOVE C4 road safety unit. The authors thanks Mr Szabolcs Schmidt and his staff
for their valuable assistance with much appreciation the efforts of Susanne Lindahl and
Maria-Teresa Sanz-Villegas in coordinating this study.
The authors also appreciate the cooperation of institutes for sharing relevant data and
inputs from colleagues who have shared their knowledge as input for the study.
The study has been presented to Member States during the CARE meeting on the 17 th
of October 2016 in Brussels.

Disclaimer
The information and views set out in this study are those of the authors and do not
necessarily reflect the official opinion of the Commission, Member States or any of the
data owners. The Commission, Member States and data owners do not guarantee the
accuracy of the data included in this study. Neither the Commission nor any person
acting on the Commission’s behalf, nor Members States nor data owners may be held
responsible for the use which may be made of the information contained therein.

October 2016

5

Study on Serious Road Traffic Injuries in the EU

Executive summary
It is the ambition of the EU to reduce the number of killed and seriously injured on the
roads over time. It has turned out that, especially in relation to serious injuries, there is still
a significant knowledge gap on how to reduce these numbers in the EU. The Commission is
therefore committed to develop a particular focus on the serious road traffic injuries, to
better understand their causes and effects. One of the first actions the Commission has
undertaken is to develop a common definition of ‘serious traffic injury’ within all Member
States as injuries scoring 3 or more on the medical Maximum Abbreviated Injury Scale
(MAIS3+). While the EU Member States are proceeding in estimating the total number of
serious injuries for their country, there is the need to know more about main crash
circumstances of MAIS3+ casualties in order to make a start with the formulation of
strategies and measures that are effective in the prevention of these injuries.
Aim of this study
The general objective of this study is to collect knowledge that will enable the future
identification of measures for effective prevention of serious road traffic injuries. The
specific objective is to provide fact-based analysis on the most common circumstances
and types of road traffic crashes leading to serious injuries of MAIS3+ severity. More
specifically, the study is directed at providing an understanding of the main circumstances
and factors that affect the emergence of serious road traffic injuries, medically coded as
MAIS3+, for the following road traffic modes in the EU: pedestrians, bicyclists,
motorcyclists and car occupants.
Approach of the study
The study has been performed on data of MASI3+ cases linked with crash information,
which was available for the following countries: Austria, Czech Republic, France, Germany,
Italy, Netherlands, Spain, Sweden and England. Data are gathered from in-depth sources,
hospital discharges, trauma registers and police records linked to medical registers.
For each database, the available main crash characteristics have been extracted and used
to define common scenarios for each traffic mode. Also the most affected body regions of
the severely injured casualties have been gathered per traffic mode and per database.
Furthermore, differences in injury patterns per crash scenario have been studied in order
to find first clues for effective measures.
Results of the study
Most common characteristics of crashes with severely injured pedestrians:
 Gender: about equal division between male/female;
 Age: elderly people and children;
 Crash opponent: cars and heavy vehicles;
 Location: urban 50 km/h road section;
 Time: afternoon and winter months;
 Contributing crash factors: looking or judgement failures, speed-related and
psychoactive substances;
 Head and upper body injuries: heavy vehicles and higher speed roads;
 Lower extremity injuries: cars and lower speed roads.
Severely injured bicyclists have the following common characteristics:
 Gender: slight to heavily male dominated;
 Age: elderly, youngsters, middle aged, children;
 Crash opponent: car, no crash opponent;
 Location: urban area, 50 km/h, intersections;

Common characteristics of severely injured motorcyclists are:
 Gender: >90% male;
 Age: youngsters and middle aged people;
 Crash opponent: car, no opponent, fixed objects;
 Location: rural and urban roads;
 Time: summer and spring;
 Contributing crash factors: failures in looking or judgement, speeding and loss
of control;
 Thorax injuries: single, fixed object, rural areas;
 Lower extremity injuries: car crash.
Common characteristics for severely injured car occupants:
 Gender: two thirds males;
 Age: youngsters;
 Crash opponent: cars, no opponent and fixed obstacles;
 Location: rural roads, speeds >70 km/h;
 Time: afternoon and winter months;
 Contributing crash factors: loss of control, speeding and psychoactive
substances;
 Thorax injuries: car to car, wearing seat belt but no airbag available;
 Head injuries: crash with fixed object and heavy vehicles, not wearing a seat
belt and no airbag available;
 Lower extremity injuries: car to car crashes, also in lower speed zones.
A first comparison with main features of fatal crashes in the EU has revealed that the
MAIS3+ results are probably quite representative for the entire EU, although it is
expected that there will be country specific differences, as was also found in some
results in this study (e.g. differences in travel purposes of certain traffic modes like
two-wheelers resulting in particular crash characteristics, differences in share of road
types and differences in shares of crash opponents which may be influenced by modal
split, travel behaviour and country characteristics). Injury patterns seem to be largely
influenced by these crash characteristics.
Recommendations
Although this study was not directed at defining effective measures to prevent serious
injuries, the findings provide support that a number of measures that are known to be
effective for the prevention of fatal crashes could also help reduce serious injuries. A
more detailed study of the causes of serious road injuries, linked to the actual policy
and the state of the road traffic system in Member States, could reveal more specific
keys to reduce the number of serious injuries in the EU.
Policy recommendations at EU level are to help Member States in creating awareness of
the specific characteristics of MAIS3+ casualties and tune their policy to the prevention of
these crashes. Research into effective measures is therefore a next important step.
Defining a severe injury target could help to increase awareness, information gathering
and policy efforts directed at the reduction of serious injuries. Benchmarking between
Member States can provide further opportunities to learn from each other.

Introduction
The costs of road safety to society are substantial and the suffering of the victims and
their families huge. Therefore, it is the ambition of the EU to reduce the number of
people killed and seriously injured on the roads over time. However, the number of
seriously injured casualties seems not to have decreased as quickly as the number of
road fatalities in the last decades. As more knowledge on road safety issues and data
of high quality becomes available, it turns out that there is still a significant knowledge
gap on how to solve the problem of serious road traffic injuries in the EU. The EU-wide
data on serious road traffic injuries have not been reliable and comparable and less
analysis has been performed on the road crashes causing serious injury than on the
fatal crashes.
The Commission is therefore committed to develop a particular focus on the serious
road traffic injuries, to better understand their causes and effects. One of the
important steps that has been made within the EU, is the development of a common
definition of ‘serious traffic injury’ within all Member States as injuries scoring 3 or
more on the medical Maximum Abbreviated Injury Scale (MAIS3+). In March 2016,
the first EU-wide estimate of the number of serious injuries was reported by the
European Commission: 135,000 in 2014. First indications are that vulnerable road
users and powered two-wheelers are dominating. Member States are proceeding now
in estimating the total number of serious injuries for their country. In the meantime,
there is the need to know more about main crash circumstances for those reported to
have MAIS3+ road traffic injuries in all Member States. Ultimately, this type of
information will be an important basis for the formulation of new road safety measures
that can be more effective in the prevention of serious injuries on the roads.
As a first step the EU Member States are working within the CARE Experts Group to
enumerate the total of seriously injured casualties according to the MAIS3+ definition.
A range of methods can be used to achieve this and the results are expected to be
available at the end of 2016. Moreover, within the H2020 project SafetyCube
(SafetyCaUsation, Benefits and Efficiency), practical guidelines are being developed for
determining the number of MAIS3+ casualties and an evaluation is being made of how
differences in methodology influence the estimated number of serious road injuries.
These guidelines should help the countries to further improve their MAIS3+ estimate
and will provide insight into the comparability of numbers from different countries. The
Commission also collaborates with FERSI in studies on uniform injury classification and
state of the art of MAIS3+ assessment in the FERSI Member States and EU/EEA
countries.
The total number of serious injuries alone will however not reveal the key factors that
relate to the causation of serious crashes and further data is needed to improve
knowledge and future policies. A second step will be to examine crash data that
includes both details of the crash circumstances and a classification of injury severity
using AIS. Some Member States are expected to have such data available in the near
future however work is still ongoing for many and alternative approaches and different
datasets must be examined to provide these first indications of serious crash
characteristics.
Systematic data describing seriously injured casualties are not available for every EU
country and therefore it is not yet possible to provide a comprehensive analysis that is
representative of all EU crashes. The extent to which the results of analyses of the
available MAIS3+ data can be generalized to the EU and the accurate specification of

October 2016

13

Study on Serious Road Traffic Injuries in the EU

the data constraints will be important aspects of this first analysis of serious crashes
and casualties.
Serious road traffic injuries are an important challenge for road safety policy making in
the near future. For this reason, the research activities within this project are focussed
on understanding the circumstances of crashes that result in serious injuries and to
thereby contribute information to help reduce the numbers of serious road traffic
injuries.

Objectives of the study
The general objective of this study is to collect knowledge and perform analyses that
will enable the future identification of measures for more efficient prevention of
serious road traffic injuries. The specific objective is to provide fact-based analysis on
the most common circumstances and types of road traffic crashes leading to serious
injuries of MAIS3+ severity.
More specifically, the study will provide information on the following issues:
 For pedestrians, cyclists, motorcyclists and car occupants respectively, what
are the most common circumstances of a road traffic crash causing serious
injury? E.g.: what other vehicles are most commonly involved, what location
and in what situation did the crash occur, what serious injuries did the crash
result in and, to the best extent possible to define, how were these injuries
sustained?
 The assessed share of serious injury crashes accounted for by each identified
most common crash scenario.
 Information on the most detailed level possible, e.g. differentiating between
the most common serious injury crash scenarios per gender, for different age
groups, crash opponents etc.
 Factors that could be found to impact the injury severity, for the crash types
and crash scenarios found to be most common for each road user group.

Overall approach
The study on serious road traffic crash characteristics aims at understanding the main
circumstances and factors that affect the emergence of serious road traffic injuries,
medically coded as MAIS3+. As only a small number of countries have such data
available at the moment, the study has some limitations in the number and
geographical spread of Member States that are part of the analyses. Nevertheless, this
study provides first main clues for policy makers for the following road transport
modes within the EU: pedestrians, cyclists, motorcyclists and car occupants.

Data and variables included in the study
Four types of data sources containing stratified MAIS3+ data have been used in the
study:
 In-depth crash data;
 Hospital discharges;
 Trauma registers;
 Police recordings of road traffic casualties which are linked to hospital data.
Furthermore, additional qualitative information available from in-depth specialists and
medical experts has been used to fine-tune and interpret results.
Table 1 shows an overview of the data sources that have been used, including data of
9 different EU member states.

Meta-data
The data that are used in the analyses need to be well understood in order to provide
a good understanding of the results of the study. For this purpose, Annex I provides
an overview of the general characteristics of each of the datasets mentioned in Table
1. It also defines how the data is gathered, the more specific geographical coverage
and the inclusion of crash types and crash characteristics.

1
2

Initiative for the GLobal harmonisation of Accident Data, data provided by BASt

Czech In-depth Accident Study, data provided by CDV. This data was produced with the financial support
of the Ministry of Education, Youth and Sports within the National Sustainability Programme I, project of
Transport R&D Centre (LO1610), on the research infrastructure acquired from the Operation Programme
Research and Development for Innovations (CZ.1.05/2.1.00/03.0064).
3
Rhône road trauma registry, France, IFSTTAR
4
5
6
7

In order to have a sufficient number of cases per database and per traffic mode, but
also to provide results based on recent data, data have been used from the period
2000-2014 unless indicated otherwise.
Variables
In order to study common crash types, crash scenarios and crash circumstances of
MAIS3+ casualties, the following variables have been used:
 Traffic mode of casualty (=pedestrian, cyclist, motorcyclist/PTW or car
occupant)
 Traffic role of casualty (=driver or passenger)
 MAIS-score
 AIS body region
 Age of casualty
 Gender of casualty
 Month of crash
 Time of day/day or night of crash
 First crash opponent (if any)
 Number of active (= non-passenger) road users involved in the crash
 Age of vehicle of casualty
 Crash type
 Road type
 Carriage way type
 Speed limit of the road
 Junction type
 Road surface conditions
 Special situation
 Other contributing crash factors
 Other contributing injury factors
 Impact severity (speed difference)
Details about each variable and its labels can be found in Annex IIa. For the
availability of each of these variables and labels per database, see Annex IIb.

Methodology
The analyses have been performed on data of MAIS3+ casualties that did not die
within 30 days (=severe road traffic injuries). As not all databases have details
available on all the variables and labels that have been defined for the study (see
Annex IIa and IIb), databases have been analysed separately in order to have as
much information available as possible. For details of preparation on the data, see
Annex III.

October 2016

16

Study on Serious Road Traffic Injuries in the EU

First of all, the main characteristics of the crash and casualties were summarised per
database for each of the traffic modes. Within variables, details with low numbers
(rare characteristics) have been merged into combined classes where necessary.
Cluster analysis has been used to determine main crash scenarios and circumstances
per traffic mode. This is done by using a Two-Step clustering method (see also Annex
III).
The results from the different databases have been merged by looking for
commonalities as a starting point and by indicating the range that has been found in
the most common characteristics. Where findings differed between databases, these
have been highlighted.

Results
Detailed results per traffic mode and per country and database can be found in Annex
IV. This section contains a summary of the results.
Pedestrians
In total, 10,31710 severely injured pedestrian casualties have been analysed, with
cases being contributed from 8 European countries: Austria, Czech Republic, France,
Germany, Italy, the Netherlands, Sweden, and the England. From these databases, we
get quite a homogenous picture of the most common crash characteristics and
scenarios, which are as follows:
Pedestrian characteristics
From the databases that have been analysed, it appears that severely injured
pedestrians are about equally often males as females, with a slight shift towards
males (45-61% male). The elderly and in some countries also children (CZ, NL,
England) are over-represented among those severely injured.
Crash opponents
Pedestrians mostly get hurt in a crash with a car: in most countries analysed,
pedestrians hit by a car accounted for 60% to 84% of all cases. A second common
crash opponent category for severely injured pedestrians is heavy vehicles (9% to
27%). In the Netherlands, powered two wheelers are the second most common crash
opponent. This is not the case in other countries. In most cases, the pedestrian is hit
by one traffic participant and in only a small number of cases, more traffic participants
are involved. The Czech, German and UK data also show evidence for common
manoeuvres and impact sides. These results show that most pedestrians get severely
injured during a forward manoeuvre (of the crash opponent), resulting in a frontal or
side collision with the pedestrian.
Location characteristics
A high proportion of the crashes resulting in a seriously injured pedestrian occur on
urban roads (86-95%) where a speed limit of 50 km/h or equivalent in mile/h is most
common. Most often, the pedestrian gets hurt on a road section, but also junctions are
quite frequent locations where pedestrians get severe injuries. In Germany, junctions
are the most frequent location type.
Time related characteristics

10

With at least 10,203 unique cases, as the RAIDS/OTS and the IGLAD database contain data of one or
more countries that are also central in one of the other databases.

October 2016

17

Study on Serious Road Traffic Injuries in the EU

A relative high proportion of pedestrians get severely injured during a crash that takes
place in the afternoon, which was found to be the most common time of severe injury
crashes in all transport modes that have been analysed. Most common months are
winter months. In the Netherlands, also spring is a period where severe casualty
numbers of pedestrians are relatively high.
Contributing crash factors
For some of the databases, there was evidence for other contributing crash factors.
For severe pedestrian crashes, the following were found to be most common:
 Pedestrian failed to look properly (48-58%);
 Pedestrian careless/reckless behaviour (22-37%);
 Driver failed to look properly (21%)/ Vision affected (driver or pedestrian;
28%);
 Pedestrian failed to judge path/seed of vehicle (19-20%);
 Pedestrian under the influence (alcohol/drugs; 14-30%);
 Speeding of the crash opponent (7-9%).
Bicyclists
In total, 37,17411 severely injured bicyclist casualties have been analysed from 8
European countries: Austria, Czech Republic, France, Germany, Italy the Netherlands,
Sweden, and England. From these databases, we get the following picture of the most
common crash characteristics and scenarios:
Bicyclist characteristics
The countries that were analysed show quite a large spread in dominant gender
involved in severe bicyclist crashes: from 55% male in the Netherlands to 83% male
in England. Elderly (CZ, FR, DE, NL), adolescents (FR, NL, SE), middle aged adults
(FR, DE, SE) and children (England) are groups that are found to be dominant in
different countries.
Crash opponents
Bicyclists in most analysed countries suffer the most severe injuries when hit by a car
(45%-68%). In some countries (FR, NL), single vehicle crashes are more common
(64%-85%). In the Netherlands, the car is the most common crash opponent in the
police data (BRON) linked to hospital data (DHD), but from the DHD trauma register,
it is known that by far the majority of severe bicycle crash injuries are crashes without
a motorised crash opponent (or at least without an opponent that hits the bicycle).
Especially these crashes appear to be largely missing in the police registration. Until
now, there are no similar results known from other countries.
Most bicycle crashes occur when moving forward, but also rounding a bend is found to
be a common manoeuvre preceding serious injury crashes.
Location characteristics
As with severely injured pedestrians, severe bicycle casualties also most often occur in
urban areas (82%-93%) on roads with a speed limit of 50 km/h (or 30 mile/h). In
most countries, intersections are in 39% to 61% of cases found to be the place for
bicyclists to get severely injured. Intersections are a conflict location where bicyclists
interact with other traffic such as cars.
Time related characteristics

11

With at least 30,237 unique cases, as the BRON-DHD/DHD traffic register, RAIDS/OTS and the IGLAD
database contain data of one or more countries that are also central in one of the other databases.

October 2016

18

Study on Serious Road Traffic Injuries in the EU

A high proportion of severe injuries are found in summer. As with the other transport
modes, bicycle crashes occur most frequently in the afternoon.
Contributing crash factors
For some of the databases, there was evidence for other contributing crash factors.
For severe bicycle crashes, the following were found to be most common:
 Failure to look properly (39-55%);
 Careless / reckless behaviour (19-28%);
 Vision affected (22%);
 Failure to judge path / speed of other road user (19%);
 Loss of control (12%);
 Poor turn / manoeuvre (12%);
 Speeding (9%);
 Red light running (7%).
Motorcyclists
In total, 9,18612 severely injured motorcyclist casualties have been analysed and
1,790 powered two-wheelers (PTW; including mopeds) for those databases where
motorcyclists could not be distinguished as a separate category. Data were available
from 9 European countries: Austria, Czech Republic, France, Germany, Italy the
Netherlands, Sweden, Spain and England. From these databases, we get the following
picture of the most common crash characteristics and scenarios:
Motorcyclist characteristics
Severely injured motorcyclists are dominated by males (91%-96%). In the databases
with PTW (CzIDAS, Rhône trauma registry and IGLAD), the share of males is
somewhat lower. Most of them (95%) are the rider of the motorcycle. Dominant age
groups are youngsters (18-24 years) and in Sweden, Germany, the Netherlands and
the UK also middle aged adults (around 40 years old). In Germany, this group of
middle aged adults is the most dominant.
Crash opponents
In most countries, cars are the most common crash opponent for severely injured
motorcyclists (42%-59%) and two active road users involved in the crash (46%67%). Single vehicle crashes and crashes into fixed objects are also very common.
Particularly in Sweden, single vehicle crashes outnumber the crashes where a car is
the crash opponent.
The impact location for severe motorcyclist crashes is most often to the front, with
side-impacts as the second most frequent. Those databases that provide information
on the manoeuvre show that a turning manoeuvre or going straight (sometimes in a
bend) are common in severe motorcyclist crashes.
Location characteristics
In some countries, rural road crashes outnumber those on urban roads: 45%-55%
rural crashes are observed in the Netherlands and Sweden. Other countries have most
severe motorcyclist crashes on urban roads: 53%-60% urban crashes are observed in
Germany and the UK but this finding may be due to biases related to the scope of indepth data sources, which mainly cover more urban areas. At least, we can conclude

12

With at least 9,119 unique cases, as the RAIDS/OTS database contain data of England which is also
covered in the STATS10-HES database. Furthermore, the databases of CziDAS, Rhône and IGLAD 1839
powered two wheelers were analysed, also including mopeds.

October 2016

19

Study on Serious Road Traffic Injuries in the EU

from this that severe motorcyclist crashes are not a major problem on motorways.
The Czech Republic data shows more crashes on urban roads, but this includes other
powered two-wheelers like moped riders, which might account for this somewhat
different pattern. All databases show that motorcycle crashes occur for 39% to 65%
on road sections.
Time related characteristics
Summer and spring are the periods where high proportions of MAIS3+ motorcyclist
crashes happen. As with all transport modes, crashes happen frequently in the
afternoon, between 3 and 6 PM.
Contributing crash factors
For some of the databases, there was evidence for other contributing crash factors.
For severe motorcycle crashes, the following were found to be most common:
 Failure to look properly (40%)/vision affected (34%);
 Speeding or inappropriate speed for conditions (26-34%);
 Loss of control (25%);
 Poor turn / manoeuvre (25-31%);
 Failed to judge path or speed of other road user (23%);
 Careless/reckless behaviour (23-43%).
Car occupants
In total, 21,55713 severely injured car occupant casualties have been analysed, with
contributions from 9 European countries: Austria, Czech Republic, France, Germany,
Italy the Netherlands, Sweden, Spain and England. From these databases, we get a
quite homogenous picture of the most common crash characteristics and scenarios,
which is as follows:
Car occupant characteristics
Of all road users that get severely injured in traffic as a car occupant, around two
thirds are male (59-69%) and about one third female. About two thirds to three
quarters of these car occupants are drivers of the car, the others are passengers. In
Germany, the share of drivers is somewhat lower than in other countries.
Furthermore, a high proportion of casualties are among youngsters.
Crash opponents
A crash with another car is one of the most common circumstances (34% to 45%) in
which car occupants get severely injured in the countries that were analysed.
Furthermore, single vehicle crashes (22%-49%) and crashes with a fixed object (1535%) are also very common but there are differences in the shares when comparing
countries: in Germany and the Netherlands most frequently car occupants are injured
in a single vehicle crash (44%-49%), followed by a car to car crash (34%-35%) and
crashes with a heavy vehicle (16%-18%). In Sweden, England and the French Rhône
region, car to car crashes are most common (37%-42%), followed by single-vehicle
crashes (England and Rhône 22%-30%) or crashes with a fixed object (15%-35%).
In most countries, the involvement of another traffic participant is most common
(45%-66%). In Sweden, the involvement of only one vehicle is about equally common
as the involvement of two vehicles. Another common finding in all datasets is that
frontal impacts are most common (61%-69%) followed by side-impacts (22%-29%) in
crashes where car occupants get severely injured. Some databases also have

13

With at least 21,296 unique cases, as the RAIDS/OTS and the IGLAD database contain data of one or
more countries that are also central in one of the other databases.

October 2016

20

Study on Serious Road Traffic Injuries in the EU

information on manoeuvres, which show that the majority of crashes that result in
severe injury for the car occupant happen during straight forward movement (64%80%).
Location characteristics
Most crashes where car occupants get severely injured occur at road sections (66%79%) and on rural roads (50%-69%). This agrees with the fact that car occupants get
particularly severely injured at roads where the speed limits are high (≥70km/h).
There are some small speed limit differences between countries, but the general
picture shows mainly a large number of severely injured car occupants with high
speed limits.
The combination of crash opponent and location characteristics is most prominent in
crash scenarios of severely injured car occupants. One of the most common crash
circumstances is where a car has a frontal collision with another car or a fixed object
on a rural road, mostly on a road section or a curve. Another common crash situation
is on urban roads where cars have a frontal or side impact collision with another car or
crash with an obstacle when going ahead on a road section or when making a turning
manoeuvre at a cross section. A third scenario is on motorways, where cars crash
against a fixed obstacle or have a rear-end crash when driving ahead.
Time related characteristics
As with all other transport modes, a high proportion of crashes in which car occupants
get severely injured occur in the afternoon. The most common period in the year in
which crashes happen varies per country, but winter months are quite common.
Contributing crash factors
For some of the databases, there was evidence for other contributing crash factors.
For severe crashes leading to severely injured car occupants, the following were found
to be most common:
 Loss of control (40-58%);
 Speeding and/or inappropriate speed (35-56%);
 Careless / reckless behaviour (23-49%);
 Driver under the influence (drugs/alcohol) (18%);
 Failed to look properly (17%);
 Road condition (wet/icy/poor surface; 14%);
 Fatigue (driver or opponent; 10%).

Methodology
To get an indication of the main injuries per transport mode, the share of injuries per
AIS body region per road traffic mode has been summarised. For each casualty, the
body region most severely injured has been used as the unit for the injury analysis.
Casualties may have had a serious (MAIS3+) injury in more than one body region

October 2016

21

Study on Serious Road Traffic Injuries in the EU

and/or they may have had multiple (MAIS3+) injuries to the same body region. Where
two or more body regions rank equally high in severity, the casualty injury outcome
has been classified as ‘multiple’.
Secondly, differences in the most severely injured body region per crash scenario have
been tested by using a Chi-square test (see Annex III). Medical and in-depth experts
have added their experiences to get first explanations for the findings.

Results
To get some feeling for the type of injuries that are coded as MAIS3+, analysis of the
in-depth databases shows the following details:
 Fractures of the head;
 Head and brain injuries;
 Fractures to the leg, ankle or foot (parts which are indicated as ‘lower
extremities’);
 Fracture of the pelvis (part of ‘lower extremities’);
 Rib fractures (part of ‘thorax injuries’);
 Organ injuries (part of ‘thorax injuries’);
 Fracture of the arms, wrists or hands (indicated as ‘upper extremities’).
Detailed results per traffic mode and per country and database can be found in Annex
IV. This section contains a summary of the results.
Pedestrians
Pedestrians get most often severely injured to the head and to the lower extremities,
but also their upper extremities often get injured. Lower extremities particularly get
injured when the pedestrian is hit by a car or in a crash on a 30 km/h zone.
Another finding is that during night time crashes the thorax and pelvis are more often
injured, while during daytime crashes, the head is more often injured, (particularly in
males) as well as the upper extremities (particularly females).
Bicyclists
Head injuries are found to be the most frequent for severely injured cyclists. From the
different databases, there is no clear profile that becomes apparent for certain types
of crashes where head injuries are more common: in some databases evidence was
found that single vehicle crashes can be associated with more head injury, but in other
databases, head injuries were found more in crashes with a car.
Some evidence was found that the lower extremities are particularly injured in single
vehicle crashes and crashes with lower impact speed (e.g. in urban areas or in crashes
where the cyclist is hit by another bike). From in-depth studies, there is evidence that
these are mainly hip fractures, which are inflicted due to an impact with the ground,
especially when the cyclist is elderly. Younger cyclists are more using their arms
during a fall in single–bicycle crashes and crashes with another bicycle. This is also
visible in the somewhat higher share of injuries of the upper extremities in these
scenarios compared to other scenarios.
In some databases, thorax injuries were seen more frequently in side-impact injuries
in urban areas and/or at junctions, when the cyclist was hit by a car than in other
situations.

October 2016

22

Study on Serious Road Traffic Injuries in the EU

Motorcyclists
For motorcyclists, the body regions most severely affected are most frequently the
thorax and lower extremities. Also head injuries and injuries to the upper extremities
are common.
Thorax injuries are most frequently found in single vehicle crashes and crashes with a
fixed object, while lower extremity injuries are particularly found in crashes with a car.
Car occupants
The most severely injured body regions for car occupants are most often found to be
the thorax, the head and lower extremities.
In some of the databases that were analysed, indications were found that injuries to
the thorax occur more often in crashes with another car and when a seatbelt is used
but the car has no airbag. Head injury also more often occurs in crashes with a fixed
object or with heavy vehicles. The study showed indications that lower extremities are
more often affected in car to car crashes and are also found more in crashes at lower
speeds (e.g. 50 km/h roads).

Representativeness of the findings for the EU
This study was based on the available databases with MAIS3+ casualties and crash
details, which turned out to be available only for countries that are more located in the
West and middle part of Europe (see map). There was no information available from
the Eastern part of Europe that could be used for this study.
Country/region covering
statistics region
In-depth cases
Small number of indepth
cases
via
IGLAD

Figure 1: Overview of data of Member States included in this study.

October 2016

23

Study on Serious Road Traffic Injuries in the EU

This brings in the question how valid the results of this study are for all Member
States of the EU. It is possible that the profile of key demographics in the countries
from which data has been analysed differs from the countries that could not be
included. From composite index research, countries in Europe appear to differ in their
profile, for instance distinguishing between countries that are mainly in the NorthWest, the centre of Europe, the East or the South (see also Wegman et al., 2008), but
also somewhat other clusters of comparable countries have been found (Bax et al.,
2012).

Methodology
To get an idea of the representativeness, the results have been reviewed qualitatively
against main fatal crash characteristics and scenarios per traffic mode, covering all EU
Member States (sources: ERSO webtexts): information of main fatal crash
characteristics and scenarios provide an EU wide picture as well as providing more
detailed information on countries that show different profiles, which can be compared
to the results of this MAIS3+ study.
It should be mentioned here that this method is speculative and only a first step in
getting an idea of what the situation in Europe looks like. The best way to get an EU
wide picture is by collecting facts and performing evidence-based analyses. With the
current attempts of the EC and Member States to collect MAIS3+ injury statistics,
such an analysis might be possible in the future.

Results
Pedestrians
When looking at the profile of crash characteristics and scenarios that are found in
fatal crashes the following findings appear:
 About 60% of the fatal pedestrian casualties in the EU are male (Pace et al.,
2012; EC, 2016a), while the MAIS3+ study found about an equal division
between males and females being severely injured (some countries more
males, other countries more females or equal shares). It might be the case
that the travel behaviour of females and males and therefore exposure is
somewhat different in the countries that were analysed, but this is just
speculation.
 Fatal pedestrian crashes in the EU are mainly among elderly (Pace et al., 2012;
EC, 2016a), while the MAIS3+ results show both elderly and children to be
dominant age groups. As there is evidence that both children and the elderly
are the main age groups that travel by foot in the EU (DaCoTA, 2012l), it is
supposed that the MAIS3+ findings are quite representative for the whole of
the EU. The somewhat different finding of mainly elderly in fatal crashes might
particularly reflect the fact that the elderly are more likely to die as the result
of their injuries (see DaCoTA, 2012l).
 Most pedestrian fatalities occur in urban areas (see DaCoTA, 2012l), and
similar results were found for MAIS3+ injuries. In both cases, there are
individual countries with a somewhat different profile.
 Most fatalities in the EU occur in the afternoon (EC, 2016a) as was also found
in the MAIS3+ study.
 Autumn and winter time have the highest frequencies of fatalities in the EU
(Pace et al., 2012; EC, 2016a), which is more or less similar with the MAIS3+
findings.

October 2016

24

Study on Serious Road Traffic Injuries in the EU

Bicyclists
When looking at the profile of crash characteristics and scenarios that describe fatal
crashes the following findings appear:
 In fatal bicycle fatalities, males are found to be the dominant group in 80% of
the cases, but large differences in countries have been reported with the
Netherlands and Belgium having a relative large share of females (30 to 40%)
while Romania and Portugal show very low shares of females (8-10%;
Candappa et al., 2012; EC, 2016b). In the MAIS3+ results, also large
differences in gender involvement were reported (55 to 85% male). However it
could be the case that countries with a larger share of male bicycle casualties
are somewhat underrepresented in this study.
 Dominant age groups in fatal bicycle crashes are found among elderly and
youngsters (Candappa et al., 2012; EC, 2016b). Also for this rider
characteristic, a similar pattern has been found in the MAIS3+ cases, but also
with differences in the most dominant age group in different countries.
 Looking at EU figures of area characteristics where most fatal bicycle crashes
occur, in 60% of the cases this is an urban area but large differences between
countries are reported here ranging from nearly 80% urban crashes in Romania
to about 20 to 30% urban crashes in Spain and even no urban crashes
reported in Estonia and Croatia (Candappa et al., 2012; EC, 2016b). In the
MAIS3+ study, urban areas have been found to be dominant as well, but with a
larger share (>80% urban) than the results of fatal crashes show.
 A high proportion of fatal bicycle crashes occur in the afternoon (Candappa et
al., 2012; EC, 2016b) and this is also the finding for the MAIS3+ crashes.
 During the summer months, most fatal bicycle crashes occur in the EU
(Candappa et al., 2012; EC, 2016b) and similar results were found for the
MAIS3+ casualties.
Motorcyclists
When looking at the profile of crash characteristics and scenarios that describe fatal
crashes the following findings appear:
 More than 90% of fatal motorcyclists has been found to be male (Yannis et al.,
2012; EC, 2016), although larger shares of female fatalities were found in
Ireland and Sweden (EC, 2016c). In general, this is a similar finding to the
MAIS3+ study.
 Fatal motorcyclists crashes are dominant in young adults and in some countries
also older riders, especially in central European countries (Yannis,et al., 2012;
DaCoTA, 2012n; EC, 2016c).
 The majority of fatal motorcyclist crashes occur on rural roads (Yannis et al.,
2012; EC, 2016c). In the MAIS3+ study, the results were not conclusive as a
number of countries reported most motorcycle crashes on urban roads (e.g.
Germany and UK) while others showed rural road crashes to be dominant (e.g.
the Netherlands and Sweden).
 During spring and summer, most fatal motorcycle fatalities occur (Yannis et al.,
2012; EC, 2016c) and a similar period found to be most common in this
MAIS3+ study.
Car occupants
When looking at the profile of crash characteristics and scenarios that are found in
fatal crashes the following findings appear:
 In crashes fatal for car occupants, about 80% of the drivers and about 50% of
the passengers were found to be male in the EU, with large differences
between countries: about 30% female drivers in Sweden and about 60%
passengers in Greece to about 5% female drivers in Bulgaria and Romania and

October 2016

25

Study on Serious Road Traffic Injuries in the EU









30% passengers in Ireland (EC, 2016d). In the MAIS3+ analysis, also a
majority of male occupants was found: about 65% of all severely injured car
occupants and a more homogenous picture was seen among the countries
analysed.
Adults (25-49) have been found to be dominant in fatal car occupant crashes
(EC, 2016d). In the MAIS3+ injuries, youngsters have been found to dominate
the data.
Fatal car occupant crashes occur mostly on rural roads (70%), although there
also are exceptions (e.g. Malta, Cyprus, Croatia; EC, 2016d). The general
picture is very similar to what was found in the MAIS3+ analysis.
Both in the fatality statistics (EC, 2016d) and in the MAIS3+ study, the
afternoon was found to be the time period where, in general, most crashes
occur.
The fatality statistics of car occupants show a fairly even distribution of crashes
over the course of the year (EC, 2016d). In the MAIS3+ study, winter months
were found to dominate.

Concluding remarks
Overview of the findings and possible explanations
This study on MAIS3+ injured pedestrians, bicyclists, motorcyclists and car occupants
has been performed on the crash data of 9 European countries. Below, we first
summarise the main findings for each of the studied transport modes.
The results that have been found do not provide direct evidence for explanations and
clues for road safety measures. In order to provide initial explanations, assumptions
have been made either linked to ‘risk’ (hazards on the road) or ‘travel behaviour’ (the
amount of time spent on the road). The assumptions are based on general road safety
expertise and findings in literature. As there is little to none literature on MAIS3+
casualties and their background, the literature used is based on general travel
behaviour patterns and other severity levels such as road traffic fatalities.
Pedestrians
Common crash factors and scenarios
Pedestrians that get severely injured in road crashes, appear to have the following
most common characteristics:
 Gender: about equal division between male/female
 Age: elderly people and children
 Crash opponent: cars and heavy vehicles
 Location: urban 50 km/h road section
 Time: afternoon and winter months
Possible explanatory factors
The pedestrian characteristics that were found may in the first place reflect both travel
behaviour as well as risk factors related to these groups: children and elderly often
participate in traffic as pedestrians (see also DaCoTA, 2012l) and they both represent
relatively vulnerable age groups, with elderly also having a higher probability of
functional disabilities with increasing age (see DaCoTA 2012k for an overview).
In general, the pedestrians specific risks in relation to all other road users are the
unprotected road use, differences in mass and speed (see also DaCoTA, 2012l).
Pedestrians get particularly injured by cars. As cars are generally found to be the
dominant group in modal split statistics (in DaCoTA 2012l), travel behaviour could be

October 2016

26

Study on Serious Road Traffic Injuries in the EU

a first explanation for the findings that pedestrians are most often severely injured in
a crash with a car.
The finding that most severe injured pedestrians result from a crash on an urban road
with a 50 km/h speed limit can have several explanations. First of all, it may reflect
travel behaviour: pedestrians may have most trips inside urban areas, where a 50
km/h speed limit is also very common. A second possibility is that crashes with a
pedestrian on rural roads are more frequently fatal, leaving more pedestrians alive on
urban roads with less severe injury on the access roads with lower speed limits (e.g.
30 km/h roads) and more severe injury on the higher speed roads (e.g. 50 km/h).
There is indeed evidence for the idea that rural pedestrian crashes are more often
fatal than urban pedestrian crashes (see DaCoTA, 2012l). Furthermore, in relation to
the posted speed limit and actual speeds on the roads, we can also look at this factor
from a ‘risk’ perspective. From crash statistics, it is known that pedestrians and other
unprotected road users sustain more severe injury and a higher probability of a fatal
outcome when they are hit at a greater speed (e.g. Rosèn et al., 2011).
In all traffic modes, the afternoon has been found as the time where a high proportion
of severe injury crashes occur. Possible explanations for this might be travel behaviour
- more pedestrian activity in the afternoon (e.g. children walking back from school) as well as to risk factors such as fatigue that builds up during the day or circadian
fluctuations (afternoon dip; see DaCoTA, 2012h for an overview). In most countries,
winter months were found to have the highest frequencies of severely injured
pedestrians, which might be explained by reduced visibility of pedestrians due to
increased hours of darkness, as well as more people traveling by car because of
low(er) temperatures. Further research could provide more evidence-based
explanations.
EU representativeness
Most of the comparisons that have been made show more or less similar patterns for
fatal pedestrians in the EU as a whole and the analysis of severely injured pedestrians
from a selection of EU countries. There might however be a slight gender bias towards
females in the MAIS3+ cases that were analysed, but further research should provide
more evidence for this. Other differences that have been found (time of day) are
expected to reflect differences between fatal and serious injury crashes but also this
would need further examination to be sure.
Common injury factors
 Injury: Head and lower extremities;
 Head and upper body parts: heavy vehicles and higher speed roads;
 Lower extremities: cars and 30 km/h roads.
Also differences have been found during night time and daytime crashes but this gave
a less clear picture over the different databases.
Possible explanatory factors
From in-depth studies, medical experts and crash tests, there is evidence that cars hit
a pedestrian first at the lower extremities. In a crash at higher speed, also the upper
body parts and the head are injured in a second impact with the bonnet of the car
(see also Martin et al., 2011). Impacts with higher speeds also increase the throw
distance and severity of the secondary impact with the ground. Low speed impacts
could be more associated with ‘hit and fall over’ whilst higher speed could be ‘hit and
thrown’.

October 2016

27

Study on Serious Road Traffic Injuries in the EU

In a crash with a truck, the pedestrian is hit higher up on the body, which has been
related to the structure of the vehicle (e.g. Zang et al., 2008).
During night-time the pedestrian may not be seen so easily, certainly because most
countries have no specific measures for pedestrians to be clearly visible (see also
DaCoTA, 2012l). Where visibility is an issue, there is a higher probability that there is
less time to brake and the impact speed may therefore be greater. If the pedestrian is
thrown and then run over by another vehicle, this will affect the location of most
severe injury, which might explain less clear findings in different databases than in
other scenarios related to crash opponents and location/speed.
To conclude: vehicle design and impact speed are supposed to be two important
factors that influence the type of pedestrian injury.
Bicyclists
Common crash factors and scenarios
Severely injured bicyclists have the following common characteristics:
 Gender: slight to heavily male dominated;
 Age: elderly, adolescents, middle aged, children;
 Crash opponent: car, no crash opponent;
 Location: urban area, 50 km/h, intersections;
 Time: summer, afternoon;
Possible explanatory factors
The gender and age pattern that has been found among bicyclists is likely to reflect at
least partly the travel behaviour of bicyclists in different countries. First of all, it is
known that in the Netherlands and Denmark, the bicycle is used much more for road
transport than in other countries (see DaCoTA, 2012l). There is also evidence that this
relates to the type of use, which differs among countries from daily use and
commuting to very occasional use (e.g. sports, shopping). Cycling has been found to
be mainly a common transport mode for older children (i.e. teenagers; see DaCoTA,
2012l), which might explain at least some of the age-related findings. The fact that for
some countries, the elderly have also been found as a group with a large prevalence
might be related to the increased population numbers of this group (demographic
development) in combination with an increasing vulnerability and functional disabilities
with growing age (DaCoTA, 2012k). Added to that, cycling is also a more risky travel
mode than car driving, requiring balance, the cyclist being some distance from the
ground, and the combination of unprotected traffic participation.
As we concluded also in the pedestrian analysis, the fact that cars are commonly
found as an important crash opponent might in the first place reflect travel behaviour.
Evidence for this possibility is found from the fact that cars are the dominant group in
modal split statistics (in DaCoTA 2012l). However, in some countries, single vehicle
cycle crashes were found to be most dominant, which holds for the Netherlands and
France (Rhône region). It is remarkable that such figures become apparent when
looking at hospital discharges or trauma registers and do not appear from official
country statistics, which might explain why the importance of single bicycle crashes in
injury crashes has for a long time be unknown (e.g. Schepers et al., 2013a).
The fact that most severe injury bicycle crashes occur on urban roads might in the
first place reflect travel behaviour of bicyclists who often use urban roads (DaCoTA,
2012l). It is also known that rural road crashes more frequently lead to fatal injury,
especially for unprotected road users such as pedestrians and bicyclists, due to higher
speeds (see DaCoTA 2012l; Rosèn et al., 2011). Also, the design of the traffic system
might play a role here, with a higher probability of severe injury where the bicyclists is

October 2016

28

Study on Serious Road Traffic Injuries in the EU

not protected from motorised vehicles (like on intersections; Schepers et al., 2013b)
or the role of infrastructure in disturbing good control of balance on the bicycle (e.g.
holes in the pavement, height differences between the pavement and the road side
can cause loss of control of the bicyclist, important for single bicycle crashes;
Schepers & Klein Wolt, 2012).
The finding that a high proportion of severe bicycle crashes occur during summer
might reflect the fact that this is most of the time and in most countries a period when
the weather is nice, which might increase the number of bicycle journeys made. As
with the other transport modes, bicycle crashes occur most frequently in the
afternoon, which might reflect both travel behaviour as well as the increase of fatigued
road user participation (see DaCoTA, 2012h for an overview).
EU representativeness
The results of the MAIS3+ bicycle casualty analysis show in general a very similar
pattern to the bicyclist fatalities in the EU. There is only some evidence that the
MAIS3+ study has a slight gender bias towards females, which means that it might be
the case that in an EU wide study, a somewhat larger share of males could be found
than in the current study. This is however still speculative and needs further study. It
is unclear what we can conclude from the fact that the MAIS3+ study had a somewhat
larger urban frequency than fatal bicyclist statistics. This might reflect differences in
the profiles of countries included to those not included, but it can as well reflect that
crashes in urban areas are less often fatal than crashes on rural roads, in part due to
lower speeds in urban areas, for which some evidence exists (see DaCoTA, 2012l).
Common injury factors
 Injury: head, lower extremities and thorax;
 Head: dominant in all crash scenarios;
 Lower extremities: single vehicle crashes, elderly people and crashes with
lower impact speed;
 Thorax: side-impact crashes in urban areas and at junctions.
Possible explanatory factors
The finding that head injuries are dominant in all bicycle scenarios that have been
identified may particularly reflect the unprotected cycling in many countries: only
Finland, Spain, Czech Republic, Iceland and Sweden have (partly) mandatory helmet
wearing laws for bicyclists. From the few databases were helmet wearing rates were
available, 5 to 30% of the severely injured bicyclists (properly) wear a bicycle helmet.
But, although a helmet provides protection to the head, from a well reported Irish
study (Fingleton and Gilchrist, 2013) we know that cyclists with a helmet are largely
protected against the effects of the impact of hitting the pavement but are hardly
protected against the first impact when hitting a car or other vehicle.
Regarding the lower extremities, most often the most severely injured body region in
single vehicle crashes, crashes with elderly people and crashes with lower impact
speed, this might have to do with a different chain of actions but may also be
correlated with these characteristics. For instance, from the Dutch in-depth studies
(Boele-Vos et al., 2016) into severe bicyclist crashes, it is known that the elderly show
a somewhat different pattern than younger people: elderly people have more
difficulties in remaining a good balance on their bicycle and when they fall (single
bicycle crash), they are less likely to defend themselves with their arms unlike
younger people. Due to this, and maybe also supported by the fact that their bones
are becoming more vulnerable, they are more prone to hip injuries, which is regarded
as part of lower extremities.

October 2016

29

Study on Serious Road Traffic Injuries in the EU

The thorax injuries are more difficult to explain and may be run-overs or a
combination with severe injuries to other body parts as well. Further study is required
in order to gain a better understanding.
Motorcyclists
Common crash factors and scenarios
Most common characteristics of severely injured motorcyclists are:
 Gender: >90% males;
 Age: youngsters and middle aged people;
 Crash opponent: car, no opponent, fixed objects;
 Location: rural and urban roads;
 Time: summer and spring.
Possible explanatory factors
The fact that men are dominating the severe injuries in motorcyclist might reflect
travel patterns (men drive more on motorcycles than women; e.g. SWOV, 2014) as
well as risk taking behaviour which is known to be more common in men than in
women (e.g. DaCoTA, 2012j) and is known to be also more common in riders of
motorcycles than other transport modes (see also DaCoTA, 2012n).
Taking more risk is also known to be more common in younger people due to their
inexperience and their tendency for thrill seeking (e.g. DaCoTA, 2012j). This might
explain why youngsters are one of the age groups found to be more dominant in
severe motorcyclist crashes. Travel behaviour can be another explanation. The
minimum age at which youngster can start driving a motorcycle differs between 16
and 18 years within the EU (e.g. DaCoTA, 2012n) and from this age, it could be
expected that crash numbers rise as well since the number of journeys undertaken on
a motorcycle increases with age. Travel patterns can also be seen as a possible
explanation for more crashes in the middle aged category. This group might be a
combination of motorcyclists that return after a period of non-motorcyclist driving and
even novice drivers who decide to take up motorcycling later in life, which involves
somewhat higher risks. This middle aged group also may be at more risk because of
the road types this group likes to ride (rural and curving roads; e.g. Jamson et al.,
2005).
As with pedestrians and cyclists, a possible explanation for a car as most common
crash opponent might be the frequency of cars in traffic (e.g. DaCoTA, 2012l).
Furthermore, cars provide a risk to relatively unprotected road users (motorcyclists do
not have a cage such as car occupants have) since the cars combine power to speed
and quite high mass. Besides cars as a common crash opponent, single vehicle
crashes with or without hitting an obstacle are also found to be very common in
severe motorcyclist crashes. As with cycling, a motorcyclist requires balance and can
easily suffer from instability when something unexpected happens or the rider
misjudges the road situation (see DaCoTA, 2012n). Fixed objects are a danger to all
road users, especially those who drive relatively unprotected at high speed. Country
characteristics and the design of the road traffic system can play a role here as
explanation for the frequency with which such crashes are found. For motorcyclists,
guard rails (especially the rail posts) can be dangerous as they are primarily designed
for preventing cars from hitting an obstacle behind the rail (DaCoTA, 2012n).
The fact that both urban and rural roads were found to be dominating, depending
upon the country where the data came from, might reflect both travel patterns
(motorcyclists, particularly older motorcyclists like to ride on rural roads; e.g. Jamson
et al., 2005) and the availability of road types (some countries are more urban than
others). Also design quality and hidden motorcycle risks (e.g. curves, obstacles and

October 2016

30

Study on Serious Road Traffic Injuries in the EU

traffic calming measures on the pavement such as speed humps) in the road design
and direct road environment might play a role here (e.g. DaCoTA, 2012n).
Summer and spring are most common time periods for severe injured motorcyclists
and this is probably related to the nicer weather and the fact that the unsheltered
transport mode is more comfortable to use when it is dry and sunny. There is indeed
evidence that the higher numbers in this period are related to travel behaviour (see
also Baughan et al., 2004; De Craen et al. 2013). As in the other transport modes, the
afternoon was found to have the highest crash frequency which might be related to
travel behaviour as well as a build-up of fatigue. Further in-depth analysis of the
motorcyclist crashes could reveal other interesting and important factors.
EU representativeness
While there is clear evidence that the use of motorcycles and the number of
motorcyclist fatalities is much higher in Southern European countries than in NorthWest European countries (Yannis et al., 2012), dominant characteristics in fatal
motorcyclist crashes seem to be quite similar to those of the studied MAIS3+ cases.
Differences between countries have been found for dominant area types and age
groups, but such differences were also found in the MAIS3+ study. From this, we
might conclude that this MAIS3+ study can be regarded as quite representative of the
EU.
Common injury factors
 Injury: thorax and lower extremities;
 Thorax: single, fixed object, rural areas;
 Lower extremities: car crash.
Possible explanatory factors
As with crashes where a pedestrian or bicyclist is hit by a car, the firs impact point is
mostly the legs (lower extremities). This can lead to the motorcyclist falling down and
landing awkwardly. Impact with the ground may cause further injury, depending upon
how the motorcyclist falls.
Motorcyclists tend to wear helmets (as this is regulated in most countries) whereas
cyclists don’t (not regulated in most countries). So we see severe head injuries
particularly to bicyclists but not so much to severely injured motorcyclists. If a
motorcyclist was not wearing a helmet the chances were he would die, and therefore
would not be apparent in a severe (non-fatal) injury study (see also DaCoTA 2012n).
In single vehicle crashes, a common injury scenario is that the motorcyclists is thrown
on his handle bars or thrown over the bike into the object. In cars, the first action
would be prevented by an airbag, but this is generally not available on motorcycles.
Car occupants
Common crash factors and scenarios
Car occupants that get severely injured in road crashes, appear to have the following
most common characteristics:
 Gender: two third males;
 Age: youngsters;
 Crash opponent: cars, no opponent and fixed objects;
 Location: rural roads, speeds >70 km/h;
 Time: afternoon and winter months;

October 2016

31

Study on Serious Road Traffic Injuries in the EU

Possible explanatory factors
Youngsters and male occupants are found to be more common among severely injured
car occupants which might be explained by travel behaviour (differences in trips and
distances travelled between men and women) as well as risk factors: young drivers
and especially young men are known to take more risks due to inexperience and thrill
seeking tendencies (e.g. DaCoTA, 2012j).
As with the other traffic modes, cars also have (another) car most frequently as the
crash opponent, which has been supposed to reflect at least partly the modal split and
the frequency of cars in traffic (e.g. DaCoTA, 2012l). Single vehicle crashes were the
second most common finding and this might be related to the traffic density in the
country (e.g. with low traffic density the probability might be higher to have no crash
opponent such as might be the case in Sweden), road design and road related risks in
the country (e.g. road surface conditions, safety of the shoulders, availability of
obstacle free zones etc.). Behavioural factors can also play a role in single vehicle
crashes (e.g. more speeding and reckless driving as a contributing factor in single
vehicle crashes, as this study shows). These characteristics are particularly relevant
on rural roads, where the posted speed limit may not always be appropriate for the
road design (e.g sharp bends in the road where high speed is permissible; Tingvall an
Haworth, 1999; Lynam et al., 2004).
Another common finding in all datasets is that frontal impacts are most common
followed by side-impacts in crashes where car occupants get severely injured. This
might be related to the differences in impact and the force at which the cage of the
car protects the occupant when hit from different sides (see also EuroNCAP norms) as
well as a reflection of the probability that a car is hit on a particular side.
Crashes with severely injured car occupants occur more frequently in winter months.
This might reflect travel behaviour (people may prefer to travel by car rather than by
other transport modes during winter) as well as risks associated with winter months
(e.g. snow, ice, larger periods of darkness and reduced visibility). The finding that a
high proportion of severe injuries were found in the afternoon probably reflects
fatigue during the day and circadian rhythm effects (the after-lunch or afternoon dip;
e.g. DaCoTA, 2012k) but other factors that need further study might be involved as
well.
EU representativeness
As with the other transport modes, for the car occupants, the general picture is that
fatality statistics provide about the same pattern as the MAIS3+ analysis. For some
variables (e.g. gender, road type) remarkable differences between countries were
found when looking at fatalities; these were not so apparent for the MAIS3+ study.
Evidence-based explanations for this are hard to give. It could be the case that the
MAIS3+ study did not include countries that divert from some of the patterns (like
Malta, Cyprus, Croatia, Romania and Bulgaria). On the other hand, the general results
are fairly comparable to that of the EU pictures presented from the fatality statistics.
Furthermore, it might also be the case that crashes resulting in fatality have
somewhat different characteristics than crashes resulting in severe injury. This might
for instance well be the case for the division of rural versus urban roads: on rural
roads, speeds are mostly higher and this increases the probability of more severe
impacts and gives an increased risk of fatality compared to crashes on urban roads.
Common injury factors
 Injury: Head, thorax and lower extremities;
 Thorax: car to car, wearing seat belt but no airbag available;

Possible explanatory factors
From trauma experts, it is known that thorax injuries (rib fractures and internal organ
injury) can be the result of seat belts and airbags that press on or hit the upper part
of the body with large force when the car crashes at high speeds. In the data that was
analysed, indications were found that injuries to the thorax occur more often in
crashes with another car and when a seatbelt is used but the car has no airbag.
The head can get injured in a car by impact to the windows of airbags in case of a
hard blow, for instance when crashing with high speed, particularly when not wearing
a seat belt and in cars without an airbag. Head injury also more often occurs in
crashes with a fixed object or with heavy vehicles, either because the impacting object
has intruded car occupant’s space, or there is partial ejection through the window of
the head onto the object (particularly in side impacts). This is also the case with large
vehicles but less likely in car to car impacts.
Lower extremities of car occupants, particularly for front seated car occupants, can get
injured by hitting the dashboard of the car. Medical experts note that it is often seen
that this causes rupture of the lower part of the leg, just below the knee. Foot and
ankle injuries can occur due to interaction with the pedals and loading via the
dashboard up through the limb can cause skeletal and joint injuries.

Discussion of the study and ideas for further research
The analysis of severely injured road users has used data sources currently available
across the EU Member States. These were chosen as firstly it is possible to distinguish
non-fatal severely injured casualties within the data using a MAIS3+ criteria, and
secondly they are able to offer at least some insight into the accident circumstances
for each of the transport modes; pedestrians, cyclists, motorcyclists and car
occupants. There are variations in the way in which data are gathered and the
databases populated and this in turn impacts upon the richness of the available data
and potentially the quality of the data.
The in-depth data sources generally contain the greatest level of detail due to the
nature of the data collection using accident investigation methods. These studies tend
to be geographically limited and provide data samples aimed at being representative
of the national picture. Data sources linking police records to hospital records benefit
from being able to provide information about the accident circumstances and injury
outcomes and have the potential to give a good national picture. However the accident
circumstance data tends to be less detailed than for the in-depth studies. Linked data
sources are also dependent upon a match being found between the hospital and police
records; this match is often made using key variables available in both data sources.
The matching process is therefore not 100% certain in all cases and an indication is
given of the confidence in the matching process. The researcher makes a judgment on
the required level of confidence for case inclusion in the analysis, but there is still a
small chance that the data sources are incorrectly matched. Hospital discharge data
and trauma register data are able to provide a rich source of injury information but
can be very limited in relation to accident circumstances.
Data have been used from each of these collection methods for the analyses
presented in this report and hence the extent to which each data source has
contributed to the results varies. Despite these limitations, the data are able to

October 2016

33

Study on Serious Road Traffic Injuries in the EU

provide the best picture possible at this time relating to severe injury accidents in
Europe.
Proposal for further research
This study gave some first clues relating to common crash characteristics, scenarios
and injury factors for MAIS3+ injuries among the most prominent traffic modes in the
EU. As the previous discussion shows, explanations for the findings and also good
understanding of the detailed mechanism that are behind the facts that have been
found are still in the phase of infancy, as is the case with most literature on road
traffic MAIS3+ injuries. Furthermore, the time available to perform and finalise this
study was limited, leaving a number of interesting questions unanswered.
The team that has performed this study would like to suggest the following interesting
issues to study further at EU level:
 Detailed research into the injury causation mechanisms, taking into account
the cause of events before the injuries occurred, differences in injury patterns
between fatal and non-fatal severe injuries and more extensive review of all
body parts that are injured by crash scenario.
 A thorough review of the influence of travel patterns and risk factors that have
been suggested in this study as possible explanations for the findings.
 A study into measures that are known to be effective in reducing (severe)
injuries as well as differences and commonalities in effective measures directed
at preventing fatalities and severe injuries.
 A policy review and benchmarking study to identify how Member State’s
characteristics and efforts have had an influence on the number and type of
severe injuries.

Recommendations
This study provides an overview of the main crash characteristics, crash scenarios and
injury factors for severely injured pedestrians, bicyclists, motorcyclists and car
occupants in a number of European countries. It provides some starting points for
further policy that is explicitly directed at the reduction of severe injuries. Although a
number of the findings need further study to really understand the detailed
mechanisms that go behind important crash scenarios and injury factors, some
preliminary recommendations can be made from this study.
Starting points for measures to prevent severe injuries
Although this study was not directed at defining effective measures to prevent serious
injuries, the findings provide support that a number of measures that are known to be
effective for the prevention of fatal crashes could also help in reducing at least some
of the serious injuries. Examples are:
 Reduction of the number of conflicts between VRU and motorised traffic:
implement sidewalks, pedestrian areas, cycling tracks, loading- and unloading
areas and time zones, separation in time by traffic lights in order to decrease
the number of conflicts with motorised traffic.
 Speed reduction to protect VRU: implement credible 30 km/h zones in urban
areas, roundabouts and plateaus on intersections in order to reduce the
probability of sustaining severe injury.
 Forgiving infrastructure to all vehicle modes: e.g. shielded or obstacle free road
sides, motorcycle-friendly guard rails and poles.
 Smooth infrastructure for two-wheeler vehicles (bicyclists, powered twowheelers): prevention of single vehicle crashes for modes were balance is an
issue; bicycles and motorcycles might benefit from sufficiently wide cycling

October 2016

34

Study on Serious Road Traffic Injuries in the EU



lanes, well maintained pavements and prevention of road surface defects such
as potholes and differences in height between the pavement and the road side.
Enforcement for the prevention of risky behaviour such as speeding and drinkdriving.

For some Member States, these measures might already be implemented on a large
scale, for other Members States, implementation might be a real challenge.
Nevertheless, the injuries show that for all Member States, improvements can be
made.
More detailed study of the causes of serious road injuries linked to the actual policy
and the state of the road traffic system in Member States could reveal more specific
keys for reducing severe injuries in the EU.
Policy recommendations at EU level:








Help Member States by creating awareness of the main crash scenarios and
injury factors that have been found for MAIS3+ pedestrian, bicyclist,
motorcyclist and car occupant casualties;
Develop further knowledge on specific MAIS3+ crash causes and effective road
safety measures;
Support Member States with advice regarding measures that could be taken to
tune policy to reduce fatalities as well as severely injured road traffic users.;
Stimulate benchmarking between Member States in order to find effective
strategies and best practices that are tuned to country specific characteristics,
and provide a forum for learning from each other;
In addition to a target for road fatalities, define a severe injury target at EU
level in order to stimulate the awareness, data collection and policy efforts to
reduce severe injuries in Member States.

Policy recommendations at national, regional and local level





Develop a disaggregated data management system (in depth, link police data
with hospital data);
Formulate targets at serious injury level;
Implement effective, evidence based measures;
Learn from other countries how data can be gathered, what specific issues
need specific attention and what effective measures can be taken.

Recommendations for further research:






Study into the travel patterns and risks that are suggested as possible
explanations for common severe injury patterns;
Review of current country characteristics that influence severe injury numbers
and specific injury patterns;
Further study of mechanisms behind severe injuries patterns;
Study of effective measures directed at severe injuries;
Benchmarking of policy efforts to reduce severe injuries.

Specified regions.
Crashes with at
least one of the
people involved
willing to
participate in the
study. Sampling
until ca. 40 cases
were included in
that region;
includes about 20%
MAIS3+
X
√
X

Specified regions.
Crashes with at
least one of the
people involved
willing to
participate in the
study. Sampling
until ca. 30-60
cases were
included in that
region; includes
15% MAIS3+
√
X
√

X

X

√

√

√

√

√

X

X

√
√
√

√
√
√

√
X
√

√
√
√

√
√
√

√
√
√

√
√
√

√
√
√
√

√
√
√
√

√
√
(√)
√

√
√
√
√

√
√
√
√

√
√
√
√

√
√
√
√

October 2016

40

Study on Serious Road Traffic Injuries in the EU

Annex IIa Variables and labels used in the study
Year of the crash: all years available within 2000-2014.
For each label, the priority is indicated. This has been used in the case that individual
cases could not be included from a database for privacy reasons of persons involved.
Variable Name
Traffic mode of casualty (based on
work description of EC and details
on CADAS, somewhat clustered)
HIGH PRIORITY

Contributing injury
factors (take the
five most
important
variables (similar
to IGLAD)

No helmet used
Helmet not properly
secured
No seat belt used
Improper use of seat belt

X
X

√
√

√
√

X
X

√
√

X
X

√
√

√
X

√
X

X
X

√
√

√
√

X
X

√
√

X
X

√
√

√
X

√
X

No child restraint used
Child restraint not properly
used or fastened
No protective clothing
(PTW)
Airbag not deployed
Ejected from vehicle
Trapped within vehicle
Contact with obstacle
(two-wheeler)
Road side not forgiving

X
X

√
√

√
√

X
X

√
√

X
X

√
√

√
X

X
X

X

√

√

X

√

X

√

X

√

X
X
X
X

√
√
√
√

√
√
√
√

X
√
√
√

√
√
√
√

X
X
X
X

√
√
√
√

√
X
X
X

X
X
X
X

X

X

X

X

X

X

X

X

X

Numeric

X

√

√

X

√

X

X

X

X

Numeric

X

X

X

X

X

X

√

X

X

Alt.

Impact severity
Delta V (in km/h)
Impact
severity Delta
V (in m/h)

October 2016

54

Study on Serious Road Traffic Injuries in the EU

Annex III: Details on methodology used
Preparation of the data
The analyses are performed on severely injured pedestrians, bicyclists, motorcyclists
(if not specified otherwise) and car occupants. Severely injured traffic participants are
defined as having injuries of MASI3+ severity and not being deceased within 30 days.
For each database, the following number of cases were available in total and per
traffic mode:
Table 2: Number of MAIS3+ cases that were analysed in this study.

Before applying any analysing method, the database has been be cleared of missing
data, which is especially a requirement for the cluster analyses used. For databases
where too much data was missing (30% to 40% of all cases) these cases are left out.
For databases with only a few data missing (<5%), imputation of missings has been
done by taking the average or mode of that variable in the database. If the amount of
missings has been in between, the Multiple Imputation procedure of SPSS23 has been
used. What the SPSS Multiple Imputation procedure does is to use the information
available in the complete variables in order to obtain an “educated guess” for the
categories to which the missing entries in the incomplete variables belong.
Specific notes on the datasets used:
Czech data: CziDAS
From the CziDAS database, cases of the years 2012 – 2015 were available.
For pedestrians, the variables Injury Factors, Impact speed and VehicleAge were not
available and the variable RoadSurface was not imputable due to too many missing
values. The final list of variables was: Month, Time, CrashOpponent, ActiveRoadUsers,
Manoevre, Age, Gender, RoadType, CarriageWay,SpeedLimit, JunctionType, Location.
Only 7 bicycle cases were available in this database and only the following variables
were analysed: Gender, Age, CrashOpponent, ActiveRoadUsers, RoadType,
RoadCondition, DayNight.

15

Powered two wheelers

October 2016

55

Study on Serious Road Traffic Injuries in the EU

In the CziDAS database, 33 powered two-wheelers could be distinguished. The group
includes 1 moped, 3 PTW type unknown and 29 motorcyclists. The following variables
were not imputable for this traffic mode: VehicleAge, CrashType, ContrInjuryFactor
and ImpactSpeed, leaving the following variables available for analysis: Role, Month,
Time, CrashOpponent, ActiveRoadUsers, Manoevre, Age, Gender, RoadType,
CarriageWay, SpeedLimit, JunctionType, Surface, Location, CrashFactor.
For car occupants, the following variables could not be imputed: VehicleAge, Age, and
ImpactSpeed. Analyses were performed using the following variables: Role, Month,
Time, DayNight, CrashOpponent, ActiveRoadUsers, Manoevre, Gender, RoadType,
CarriageWay,
SpeedLimit,
JunctionType,
Surface,
Location,
CrashFactor,
ContrInjuryFactor.
French data: Rhône road trauma registry, France, IFSTTAR
For the pedestrian data the variable DayNight had 5,6% missing cases, which have
been imputed as the most common variable, which is Daytime.
For the bicycle data, the variables DayNight with 127 missing cases (21.4%) and
Helmet use with 128 unspecified cases (21.5%) have been imputed.
Since the Rhône data consisted of only a small number of motorcyclist cases, these
could not be delivered as data to the Consortium, but where included in the data of
powered two wheelers.
For the car data, the variables Seatbelt and Airbag contain 12% and 27% missing
data, which have been imputed by the Multiple Imputation method.
German data: GIDAS
From the GIDAS data, information of the years 2005 – 2016 was used.
Notes to the coding of variables
 CrashOpponent: The first crash opponent is encoded, which means the first
contact of the casualty during the crash- with an opponent, object, the road
etc.
 Vehicle Manoeuvre: The Manoeuvre was encoded using the 3-digit Crash Type.
This holds some uncertainties which participant was in which role of the crash
type. For example if the manoeuvre is coded turning or overtaking in a car to
motorcyclist crash it is not necessarily clear who actually was the
turning/overtaking traffic participant...
 Crash Factor: The Crash factor was encoded using the main crash causation
but it is not explicitly known which participant caused it. E.g. if the Crash factor
is “driver under influence” in a car to moped crash it is no certain who was
under influence...
For the clusters analyses, the following variables could be used, leaving out the
variables that were not available for that particular traffic mode or had a missing rate
larger than 20%:
 For pedestrians: Month, Time, CrashOpponent, ActiveRoadUsers, CrashType,
Age, Gender, RoadType, CarriageWay, SpeedLimit, JunctionType, Surface,
(special situation of the) Location, CrashFactor;
 For bicyclists: Role, Month, Time, CrashOpponent, ActiveRoadUsers,
Manoeuvre, Age, Gender, RoadType, CarriageWay, SpeedLimit, JunctionType,
Surface, Location, CrashFactor;

Data of the Netherlands: BRON linked with DHD, and DHD traffic register
For the traffic participants that sustained severe injuries in the Netherlands, the BRON
database linked to the DHD traffic register was used primarily. As this database is
known for a reasonable quality regarding motorised vehicles, there was no need to do
similar analyses on the DHD traffic register only for motor vehicles. Linkage with the
finding of BRON-DHD indeed showed similar results.
For the severely injured bicyclists, BRON-DHD is known for a large underreporting of
single bicycle crashes. Therefore, DHD traffic register data was used here as well in
order to get additional information of certain variables such as age, gender and type of
crash opponent (motorised vehicle versus non-motorised vehicle). As the DHD traffic
register does not contain location information, BRON-DHD is the only source for this
and these results could not be validated to the larger DHD traffic register database.
Swedish data: STRADA
Hospital and Police data have been combined in order to derive as many of the
required SUSTAIN variables as possible. Tables are matched using a common crash
reference table and only those where the level of match (Q-Value) was at least 65/100
– in these cases the match between records is considered successful.
Crash opponent was missing for around 30% of the car occupant cases, however a
cross tabulation of crash opponent against an alternative description of the crash
scenario showed that 99 consistently appears as a ‘single vehicle crash’. Single vehicle
crashes have been added as a category but it cannot be assumed that these had no
non-vehicle impact partner.
England data:
STAT19 linked HES
These databases were used to find common crash circumstances and scenario’s.
RAIDS and OTS
These databases were combined, providing 65 pedestrian cases, 18 cyclist cases, 67
motorcyclist cases and 148 car occupant cases. The combined datasets were used for
common crash characteristics in addition to the STATS19 linked HES data, crash
scenarios and the analysis of injury factors.
Crash opponent was vague in the OTS dataset – coded as ‘vehicle’ and the further
layer of detail missing. This is considered an important, high priority variable and so
each case was reviewed by reading the text file describing the crash scenario in order
to provide this variable for the analysis.
IGLAD database
IGLAD data were used of the period 2007-2014. From the IGLAD database, only the
data of European countries was analysed. The data originate from the countries
Austria, Czech Republic, Germany, France, Italy, Sweden, and Spain and were
analysed together, not per country. The table below shows the availability of cases per
traffic mode and per country:

October 2016

57

Study on Serious Road Traffic Injuries in the EU

Table 3: Number of MAIS3+ cases in the IGLAD database per country and traffic mode.

Main characteristics of crashes
To get the best results from the MAIS3+ databases, each of the included variables per
traffic mode was analysed by the descriptives procedure of SPSS. To get to the main
crash characteristics per traffic mode and prevent relative small or meaningless
categories, characteristics with small numbers were merged with other characteristics
and given a meaningful name by a road safety expert.
For example, if the results of crash opponent contained only a few cases such as
moped-car (a rare type of car), this was merged with car; the relative small numbers
of powered two wheeler subtypes were merged into one category ‘powered two
wheelers’; low numbers of crash opponents such as agricultural vehicles, busses,
trams were merged with trucks into the category ‘heavy vehicles’. Characteristics with
low numbers differ per transport mode and per country (database).

Crash types and crash scenarios
To get the main crash scenarios, we used TwoStep cluster analysis in SPSS (see IBM
SPSS Statistics Base 23). Generally, cluster analysis is a set of techniques for the
classification of objects or individuals (in our case: injured road users) into a number
of homogenous clusters. Usually, the objects or individuals have all been scored on a
number of characteristics or variables (such as crash type, gender, speed limit, etc. in
our case). In a first step the scores of the individuals on these variables are used to
calculate the distance between each pair of individuals in the dataset, pairs with very
similar scores resulting in a small distance and pairs with very different scores yielding
a large distance for the corresponding pair of individuals. In a second step all the thus
obtained distances are used to determine an optimal set of clusters of individuals
simultaneously satisfying the following two properties:
 Individuals within each cluster should have pairwise distances that are as small
as possible;
 Individuals in different clusters should have pairwise distances that are as large
as possible.
For further technical details on cluster analysis we refer to IBM SPSS Statistics Base
23 (2014, Chapters 23 and 24) and Everitt et al. (2011).
The important advantage of this classification technique compared to K-Means and
Hierarchical clustering is that it can handle variables of different measurement levels

October 2016

58

Study on Serious Road Traffic Injuries in the EU

(i.e., both continuous and categorical variables) and that it does not require the user
to provide an a priori number of clusters to be found. It is possible to provide a
maximum number of clusters to be found though, and we always set this maximum to
6 for pragmatic reasons.
In deciding whether a satisfactory cluster solution had been found we considered the
following diagnostics provided by the TwoStep Cluster analysis procedure in SPSS:
1. The Akaike Information Criterion (AIC) value of the solution, where a lower AIC
value indicates a better fitting solution;
2. The Cluster Quality of the solution which ranges from -1.0 to +1.0; we only
accepted solutions whose Cluster Quality was qualified at least as “Fair”
(Cluster Quality > 0.2) and preferably as “Good” (Cluster Quality > 0.5);
3. The predictor importance of each variable which ranges from 0 to 1. We
generally kept the variables with a predictor importance close to or equal to 1
and dropped the variables with a low predictor importance. Repeating the
analysis with a thus selected smaller number of variables usually yielded
solutions with a lower AIC and a higher Cluster Quality score;
4. We also checked whether the obtained cluster sizes were not too skewed, e.g.,
we considered a solution where the largest cluster included more than 10 times
more cases than the smallest cluster as too skewed to present most important
crash scenario’s.
Clusters that are found can be described best on the basis of the values that are most
common in that particular cluster. This means that the description of a cluster is a
simplified version of all circumstances that are included.

Injury factors
To analyse the main injury factors of MAIS3+ casualties, first the AIS body part most
heavily affected per casualty has been summed per traffic mode. Secondly, the share
of most heavily affected body parts has been calculated per transport mode and within
each transport mode per crash scenario.
In order to see whether crash scenarios accounted for different injury patterns, the
results have been analysed per transport mode. Since “cluster” and “injury type” are
both categorical variables (i.e., variables with values whose only purpose is to keep
the categories apart, and nothing more) the standard procedure to investigate their
possible relationship is to apply a Chi-square test to the contingency table containing
the cell frequencies of the categories of the two variables. To this end, the frequencies
are calculated which would have been obtained if the two variables were completely
unrelated, and the latter frequencies (known as expected frequencies) are then
compared with the observed frequencies. The larger the differences between the
expected and observed frequencies the sooner the Chi-square test will be significant,
indicating that the two categorical variables are indeed related. A significance level of
< 0,05 has been used. For further details concerning Chi-2 tests we refer to any
handbook on statistics.
Differences in results between transport modes and between scenario’s have been
analysed using the knowledge from in-depth databases, road safety experts and
medical experts.

October 2016

59

Study on Serious Road Traffic Injuries in the EU

Annex IV: Detailed results
Pedestrians
This section provides the most common crash characteristics and scenario’s, and the
injury factors of MAIS3+ injured pedestrians per country for which this information is
available.
Czech Republic
Crash characteristics
Of the 26 pedestrian casualties in the CziDAS database nearly equal shares of males
and females were involved (see Figure 2). A prominent group are children (38%;
Figure 3).
In about three quarter of the cases a car was the first crash opponent, in the other
cases it was a heavy vehicle (Figure 4) and most of the time two road users were
involved (85%). Three quarter of the casualties were a side-impact collisions. Most of
the crash occurred in urban areas (Figure 5) with a speed limit of 50 km/h (88%) and
two third of the crashes happened on road sections (Figure 6), one third at a junction.
Three quarter of the crashes happened in not physically divided two-way traffic and
the special situation of the location was defined by a VRU crossing (38%), a bus stop
(23%), no special situation (23%) or the pedestrian was behind/between parked cars
(12%).
Concerning the crash factors, in most of the cases inadequate information acquisition
of one of the participants contributed to the crash (61%), in one third the opponentdriver was under influence. The majority of crashes can be described by the
manoeuvre of going ahead other or a round curve (77%), e.g. a vehicle driving along
a road section and a pedestrian crossing the road. In 19% the opponent vehicle hit
the pedestrian in a overtaking process.
The majority of casualties were seriously injured in January and August; Figure 7).
Most of the crashes happened during daytime, mostly during commuter times (early
morning, early afternoon; Figure 8).
Crash scenarios
A first TwoStep Cluster analysis of the pedestrian data with 26 cases and the above
stated variables yields a 4 cluster solution with an AIC of 713 and a cluster quality
labelled as poor (0.2). This solution suggests the removal of Month, Time, Age, and
CrashFactor.
The second round yields a 4 cluster solution with an AIC of 358 and a cluster quality
labelled as fair (0.4). This solution yields to the third round with the variables Crash
Opponent, CarriageWay, CrashFactor and Location.
The third round yields a 4 cluster solution with an AIC of 155 and a cluster quality
labelled as good (0.5). The variables (predictor importances) are Location (1.0),
CarriageWay (0.74), CrashOpponent (0.53), and CrashFactor (0.1). The most common
scenarios that were found (see Table 4) can best be summarised as:
 Pedestrian hit by a car at a VRU crossing with undivided driving directions in a
situation with inadequate information acquisition (7 of 10);

October 2016

60

Study on Serious Road Traffic Injuries in the EU







Pedestrian hit by a car on a road with undivided driving directions without any
further specific characteristics in a situation with inadequate information
acquisition (3 of 7);
Pedestrian hit by a bus at a bus stop on a road with undivided driving
directions in a situation where one of the traffic participants is under influence
of alcohol (2 of 6);
Pedestrian hit by a car in the vicinity of a parked car on a road with undivided
driving directions in a situation with inadequate information acquisition (3 of
3);

Injury factors
Body regions most commonly injured in pedestrians in the Czech Republic are the
head (42%) and the lower extremities (31%; see also Figure 9).
The Chi-square test for the cross table of injury type by scenario is not significant
(Chi-square =28.875, df =21, p<0.117), indicating that for pedestrians there is no
significant relationship between the injury type and the crash scenario.

October 2016

61

Study on Serious Road Traffic Injuries in the EU

England
Crash characteristics
STATS19-HES - The STAS19-HES linked dataset comprises 6,355 severely injured
pedestrians. There is a bias towards male MAIS3+ pedestrians (61%) compared to
female (39%; Figure 2). There is a high proportion of children among the casualties,
with the age distribution being skewed towards younger casualties (Figure 3). There is
another peak in the data for the elderly (75 to 90 years).
The most common crash opponent is a car (80%). The next most frequent opponent is
a heavy vehicle (10%) (Figure 4). The vast majority of crashes involved one road
user, defined as the number of vehicles in the crash (93%). In almost all cases, the
opponent vehicle is moving forward without turning or overtaking. Ten percent of
crashes occurred when the opponent vehicle was reversing.
Road type (Urban/Rural/Motorway) has been derived from the road classification and
speed limit in this dataset and therefore the distribution is approximate. The indication
is that the vast majority (95%) of the crashes with MAIS3+ pedestrians occur in an
rural environment (Figure 5). Considering any junction layout, the most common
scenario is where no junction is present (45%) whilst 37% of the crashes occur at T/Y
or staggered junctions (Figure 6). The road surface was dry in almost three quarter of
cases.
The months that appear with the highest frequency are October, November, December
and January, winter months with fewer daylight hours (Figure 7). There is a distinct
peak in the number of crashes during mid to late afternoon (Figure 8).
The most crash common factors are;
 Pedestrian failed to look properly (58%)
 Pedestrian careless/reckless behaviour (22%)
 Driver failed to look properly (21%)
 Pedestrian failed to judge path/seed of vehicle (19%)
 Pedestrian under the influence (alcohol/drugs) (14%).
RAIDS/OTS - The RAIDS/OTS dataset comprises 65 severely injured pedestrians.
There are proportionally more male casualties (55%) than female (45%; Figure 2).
Age data is only available for the RAIDS data (n=14) – in this small sample there is an
even distribution of age category, child < 16 (5), adult (5) and senior > 60 (4).
The most common crash opponent is another car (78%). The next most frequent
opponent is a bus (10%) (Figure 4). Heavy vehicles (Buses and Truck) account for
15% of the impact partners. The vast majority of crashes involved the pedestrian and
one other road user (88%).
Considering the road type (Urban/Rural/Motorway) the vast majority (92%) of the
crashes with MAIS3+ pedestrians occur in an urban environment (Figure 5). This is
also reflected in the speed limit distribution where almost 84% are in a 30mile/h
speed zone. Considering any junction layout, the most common scenario is where no
junction is present (69%) whilst 20% of the crashes occur at T/Y or staggered
junctions (Figure 6). However, almost half, 46% of the crashes resulting in a seriously
injured pedestrian occurred in the vicinity of a pedestrian crossing facility. The road
surface was dry in almost 72% of cases.
Considering the lighting conditions, 62% of the crashes occurred during the daytime.

October 2016

62

Study on Serious Road Traffic Injuries in the EU

Looking closer into the conditions that were found to contribute to the crash, the
following common factors were found in pedestrian crashes:
 Pedestrian failed to look (48%)
 Pedestrian careless or reckless behaviour (37%)
 Vision affected (driver or pedestrian) 28%
 Pedestrian failed to look to judge vehicle speed / path (20%)
The crash opponent failed to look properly in 19% of cases and speed was a factor in
17% of cases.
Crash scenarios
STATS19-HES - A first TwoStep Cluster analysis of the Pedestrian data with 6,355
cases was undertaken using the nominal variables Month, Time, opponent, Gender,
Junction, Surface, Manoeuvre, ActiveRoadUsers, pedestrian_crossing and SpeedLimit
and the interval variable Age, a total of 11 input variables. This resulted in a 4 cluster
solution with an AIC of 132562.4 and a cluster quality labelled as Poor.
A second TwoStep Cluster analysis used input variables from the first with a predictor
importance > 0.5 (removing gender, speedLimit, ActiveRoadUsers and opponent). A 5
cluster solution was returned with an AIC of 99523.711 and a cluster quality labelled
as Poor. In this solution Time had a predictor importance < 0.5.
A third analysis inputted the 6 variables Month, Junction, Surface, Manoeuvre,
pedestrian_crossing and Age. A 5 cluster solution, still labelled Poor and with and AID
of 61334.8 was returned. Age had a predictor importance < 0.5 and was removed.
The remaining 5 variables produced a 4 cluster solution labelled Fair and with AIC
60467.6. In this solution Manoeuvre had a predictor value < 0.5 and a further analysis
was performed with the 4 variables Month, Junction, Surface and Pedestrian_crossing.
These gave a 3 cluster solution again labelled fair with an improved AIC 50376.5.
Removing Pedestrian_crossing (predictor < 0.5) gave a solution labelled Poor and so
previous the 4 variable 3 cluster solution was chosen. The details are (see Table 5):
 Pedestrians is hit on a T/Y or staggered junction with no pedestrian crossing
facility, in dry conditions (1642 of 2379 cases);
 Pedestrian is hit on a road section during dry conditions (2076 of 2185 cases);
 Pedestrian is hit on a road section in wet conditions (739 of 1791 cases).
The pedestrian’s failure to look properly is by far the most reported factor. The driver
failing to look is most common in the T/Y/Staggered junction cluster.
Table 5: Crash scenarios for England pedestrian data (STATS19-HES).
Cluster nr.
N
Junction

2
2379
T/Y/Staggered junctions
(69%)

3
2185
Road Section (95%)

1
1791
Road Section (43%)

Surface

Dry (100%)

Dry (100%)

Wet (96%)

RIADS/OTS - A first TwoStep Cluster analysis of the Pedestrian data with 65 cases was
undertaken using the nominal variables Roadtype Speed DayNight, opponent, Gender,
Junction, Surface, ActiveRoadUsers, and pedestrian_crossing a total of 9 input
variables. This resulted in a 4 cluster solution with an AIC of 558.465 and a cluster
quality labelled as Fair. However, roadtype showed as the most important predictor
and since only 5 of the crashes occurred in rural areas, the cluster size ratio was very
high. Roadtype (and speed) were removed and the analysis repeated with the
remaining 7 input variables.

October 2016

63

Study on Serious Road Traffic Injuries in the EU

This returned a 4 cluster solution labelled fair (AIC 465.23) with Surface, Gender and
DayNight having predictor importance > 0.5. A further analysis used these latter 3
variables and resulted in a 4 cluster solution labelled good (AIC 91.901) with all three
variables having importance > 0.5. Cluster analysis of the RAIDS/OTS data split the
data into four relatively evenly split scenarios based upon Surface conditions, time of
day and gender (see Table 6):
 Wet/Damp at night and even split of gender (9 of 18 cases)
 Dry at Night with 75% male (12 of 16 cases)
 Dry during the day all female (16 of 16 cases)
 Dry during the day all male (15 of 15 cases)
There are indications for differences in contributory crash factors between the clusters.
In all clusters, the failure of the pedestrian to look properly is a major factor. In
clusters featuring male pedestrians, the actions of the pedestrian dominate the
contributory factors, including being under the influence and for the night-time lack of
high visibility clothing. For clusters with female pedestrians factors relating to the
vehicle driver are more apparent (failed to look, distraction – daytime accidents,
speed and under the influence – night-time accidents).
Injury factors
Looking at the severe injuries MAIS3+ pedestrians in England sustain most injuries to
the lower extremities (48%) and the head (32%; see also Figure 9).
Chi-square tests of association have been applied to establish if there is any
association between cluster membership and injury type. This turned not out to be
significant.
Looking at the injury types across the clusters lower extremity is the most frequent in
the crashes during nighttime and crashes with female pedestrians. Crashes with males
have a higher proportion of head injury type than injuries with female pedestrians.
Table 6: Crash scenarios and injured body regions for England pedestrian data (RIADS & OTS).
Cluster nr.
N
Road surface
condition
Time of day
Gender
Injury type
Head
Thorax
Upper
Extremities
Lower
Extremities
Multiple regions

France, Rhône region
Crash characteristics
From the Rhône region, data of 626 severely injured pedestrians were analysed. The
data showed that slightly somewhat more males than female pedestrians are severely
injured (see Figure 2). Elderly people are dominating the number of serious pedestrian
injuries (see Figure 3). By far most pedestrians get severely injured in an crash with a

October 2016

64

Study on Serious Road Traffic Injuries in the EU

car (75%). Heavy vehicles are the second most common crash opponent (11%; see
Figure 4). Most of the pedestrian crashes (ca. 70%) occur during daytime (Figure 8).
Crash scenarios
A first TwoStep Cluster analysis of the nominal variables DayNight,
FirstCrashOpponent, DemograpyAgeGroup and DemographyGender yields a 4 cluster
solution with an AIC of 3802.980 and a cluster quality labelled as Fair. In this solution
the
variables
DayNight,
FirstCrashOpponent,
DemograpyAgeGroup
and
DemographyGender have a predictor importance of 1.0, 0.95, 0.08 and 0.65,
respectively.
A second TwoStep Cluster analysis only using the variables DayNight,
FirstCrashOpponent and DemographyGender yields a 4 cluster solution (see Table 7)
with an AIC of 1027.552 and a cluster quality labelled as Good. In this solution the
variables DayNight, FirstCrashOpponent and DemographyGender have a predictor
importance of 0.85, 1.0 and 0.57, respectively.
The four clusters or crash scenarios could be described as follows:
 Male pedestrian hit by a car during daylight (174 of 174 cases);
 Female pedestrian hit by a car during daylight (166 of 166 cases);
 Male pedestrian hit by a heavy vehicle during daylight (31 of 162 cases);
 Male pedestrian hit by a car during night time (86 of 145 cases);
Table 7: Crash scenarios and injured body regions for the Rhône region pedestrian data (Rhône road trauma
registry, France, IFSTTAR).
Cluster nr.
N
First
crash
opponent
Time of day

Injury factors
The lower part of the Table displays the frequencies in the cross-table of injury type
by cluster number. Pedestrians in the Rhône region suffer mostly from injuries of the
lower extremities (45,5%), followed by injuries of head and face (24,0%) and injuries
of the upper extremities (12,8%; see also Figure 9).
Since there is only one Face injury in this data set we added this to the Head injuries.
Moreover, there are only 9 injuries of the Abdomen and pelvic contents in this data set
and only 12 Spine injuries. We therefore dropped them from further analysis because
these two types of injuries result in 8 cells in the cross-table with expected

October 2016

65

Study on Serious Road Traffic Injuries in the EU

frequencies smaller than 5 cases. The Chi-square test for the cross-table of the
remaining five categories of injury type by cluster number is very significant (Chisquare = 54.467, df = 12, p < 0.0001), indicating that for pedestrians there is a
significant relationship between the variables injury type and cluster number.
There appear to be significant differences (see Annex IV for more details) in the most
common type of injury and the four most common pedestrian crash scenarios that
have been described in the previous chapter. The most important differences are:
 Lower limb injuries are most common in crashes with a car.
 Injuries to the head and face are nearly as common as lower injuries in crashes
where pedestrians get hit by a heavy vehicle.
 Thorax injuries are more common when pedestrians get hit by heavy vehicle
than by a car.
Germany
Crash characteristics
In total 175 MAIS3+ pedestrian casualties were found in GIDAS that were included in
the analysis. Nearly the same shares of male and female pedestrians were severely
injured in an crash (see Figure 2). The largest numbers of injured pedestrians were
among the elderly (60+) (see Figure 3).
In about three quarter of the pedestrian crashes the first crash opponent is a car. In
14% of the crashes, heavy vehicles (bus, truck, rail, agricultural vehicles) are involved
(see Figure 4). In 90% of the pedestrian crashes one other road user is involved.
About 80% of the crashes the pedestrian was hit at his side (“side-impact collision”)
and collisions were the pedestrian was hit on his front side (“head-on collision”) are
common.
Most pedestrian crashes leading to severe injury occur in urban areas (see Figure 5),
the majority on 50 km/h or 60 km/h roads with not physically divided two-way traffic.
two third of the pedestrian crashes resulting in severe injury occur at junctions (see
Figure 6) and about two third on dry road surface. Nearly half of the pedestrian crash
occur at no special location, but about each one quarter occur at bus stops and VRU
crossings and 6% with the pedestrian behind or between parked cars.
Regarding Crash Factors in 84% of the cases inadequate information acquisition 16 of
one of the participants contributed to the crash, followed by speeding (of the
opponent) with 9%.
During winter (October to February), the highest number of pedestrians get severely
injured (see Figure 7). Most crashes occur in the afternoon between 3:00 PM and 6:00
PM (see Figure 8).
Crash scenarios
A first TwoStep Cluster analysis of the pedestrian data with 175 cases and the above
stated variables yields a 6 cluster solution with an AIC of 4423 and a cluster quality
labelled as poor. In this solution only the variables Location, Speed limit, Gender,
Junction type, Type of carriageway have a predictor importance larger than 0.4.

16

Inadequate information acquisition consists of actions like: heavy braking of the vehicle in front without
compelling reason, overtaking though traffic situation is not clear, mistake during u-turn or reversing
wrong behavior towards pedestrians at pedestrian crossings, wrong behavior of the pedestrian (ignoring the
road traffic), other mistakes of the driver (very common).

October 2016

66

Study on Serious Road Traffic Injuries in the EU

A second TwoStep Cluster analysis with the variables Location, Speed limit, Gender,
Junction type, Carriageway yields a 2 cluster solution with an AIC of 1233 and a
cluster quality labelled just fair. In this solution only the variables Location, Junction
type and Type of carriageway have a predictor importance larger than 0.4.
A third TwoStep Cluster analysis only applied the variables Location, JunctionType,
CarriageWay yields a 6 cluster solution with an AIC of 309 and a cluster quality
labelled as good. In this solution the variables have an acceptable predictor
importance (1.0 JunctionType, 0.94 Location, 0.66 CarriageWay). The clusters could
best be summarised as (see Table 8)
 Pedestrians on a road section where the two-way traffic is not physically
divided and no special infrastructural situation applies (55 of 55 cases);
 Pedestrians on a road section where the two-way traffic is not physically
divided, at a bus stop (32 of 32 cases);
 Pedestrians on a cross-road where the two-way traffic is not physically divided,
at a VRU crossing facility (16 of 29 cases);
 Pedestrians on a road section where the two-way traffic is physically divided, at
a VRU-crossing facility (6 of 25 cases);
 Pedestrian on a road section where the two-way traffic is not physically divided,
behind parked cars (9 of 19 cases);
 Pedestrian on a T- or staggered junction in no special infrastructural situation
where the two-way traffic is not physically divided (11 of 16 cases).
Injury factors
Table 8 shows the most common crash scenarios including the frequencies of the body
region that are MAIS3+ injured per scenario. Most common pedestrian injuries in
Germany are the lower extremities (47%), followed by the head (26%) and thorax
(13%; see also Figure 9).
The Chi-square test for the cross table of injury type by scenario is not significant
(Chi-square=31.31.311 df=30, p<0.4), indicating that for pedestrians there is no
significant relationship between the injury type and the crash scenario.
Having a closer look into the injury distribution within the 6 scenarios shows:
 The lower extremity injuries always have a share of about half of the injuries in
each scenario except for the VRU crossing scenario with physically divided
roadways (e.g. by a tram station in the middle of the road) where the share is
17
only one third but where we find the highest share in thorax injuries (24%)
and multiple serious injuries (16%).
 The highest share of head & face injuries is found in the scenario of a
pedestrian crossing behind/between a car (42%).
For the 176 pedestrian casualties 1100 single injuries (mean of 6 injuries per casualty)
were recorded of which 310 injuries (mean of 2 injuries per casualty) have a severity
of AIS08=3 or larger. The majority of fractures were caused by the contact with the
opponent car front (118/180=65.6%) or the contact with the road and environmental
features (e.g. the curb) (44/180=24.4%). These causes are also the main causes for
all injury types, that is 82/310=26.5% of all injuries were caused by hitting the road,
198/310=63.9% were caused by the impact to opponent which was in most cases a
car.

17

The numbers relate the share within each scenario, not the share on the total number of injuries of this
type.

Netherlands
Crash characteristics
The data from 1,962 pedestrian cases in the Netherlands18 showed that males and
females are about equally involved in MAIS3+ pedestrian crashes (see Figure 2).
Especially elderly road users (70 to 85 years of age) and children (3 to 10 years of
age) sustain severe injury (see Figure 3).
By far most pedestrians get injured in an crash with a car (>60%). Powered twowheelers, vans, heavy vehicles and bicycles are other important crash opponents (see
Figure 4). Most pedestrian crashes involve one other road user.
18

Linked police-hospital data (BRON-LMR).

October 2016

68

Study on Serious Road Traffic Injuries in the EU

Most MAIS3+ pedestrian crashes occur on urban roads (see Figure 5), with most of
them having a speed limit of 50 km/h, but also 30 km/h roads and rural 80 km/h
roads were found to be common. More MAIS3+ pedestrian crashes occur on road
sections than on junctions (Figure 6) and in dry road surface conditions.
In January to March, May and June more MAIS3+ pedestrian crashes occur than in
other months (see Figure 7). Most of the pedestrian crashes occur in the afternoon,
especially between 3:00 PM and 6:00 PM (see Figure 8).
Crash scenarios
Based on the frequency tables of the variables within the linked BRON-DHD data (see
Annex IIb) it was decided only to use the variables Month, Time, Crash opponent,
Number of active road users, Crash type, Age, Gender, Road type, Speed limit,
Junction type, Surface condition for each of the transport modes for cluster analysis.
The variables Type of carriageway, Special situation, Contributing crash factors, and
Contributing injury factors had missing data ranging from 50%-85%, 73%-94%,
41%-67%, and 79%-100%, respectively. We did not make a further choice
beforehand in order to let the most common crash scenario’s appear from the
analysis.
The TwoStep Cluster analysis method (see below for further details) uses a case wise
deletion approach to missing data, implying that each case with at least one missing
value is automatically completely excluded from the analysis. Since the proportion of
missing data, if any, in the remaining variables was only approximately 5% or less, it
was decided to impute the mean of the variable if the variable was numerical, and the
mode if it was nominal.
A first TwoStep Cluster analysis of the pedestrian data with 1,962 cases and the
nominal variables Month, Time, CrashOpponent, CrashType, Gender, RoadType,
Junction, Surface, and SpeedLimit and the interval variables ActiveRoadUsers and Age
yields a 2 cluster solution with an AIC of 41,766.593 and a cluster quality labelled as
just Fair. In this solution only the variables RoadType and Speedlimit have a predictor
importance larger than 0.9, while the predictor importance of CrashType is only 0.18
and that of the remaining variables is even lower than that.
A second TwoStep Cluster analysis only using the variables RoadType, Speedlimit and
CrashType yields a 4 cluster solution with an AIC of 1759.240 and a cluster quality
labelled as Good. In this solution the variables RoadType and SpeedLimit have a
predictor importance of 1.0, while the predictor importance of CrashType is almost
0.8. Unfortunately, the ratio of the largest cluster size with 1326 cases and the
smallest cluster size with 92 cases in this solution is 14.4 which is quite skewed.
A third TwoStep Cluster analysis only applied to the variables RoadType and
SpeedLimit which had a predictor importance of 1.0 in the previous analyses yields a 3
cluster solution with an AIC of 1663.611 and a cluster quality also labelled as Good. In
this solution both variables have a perfect predictor importance of 1.0. These 3
clusters are described in Table 9 in order of cluster size.
So to summarise, analysis of main crash scenarios revealed that severe pedestrian
crashes particularly occur in the following conditions:
 Pedestrian crash on a urban 50 km/h road (1355 of 1355 cases);
 Pedestrian crash on an urban 30 km/h road (280 of 368 cases);
 Pedestrian crash on a rural 80 km/h road (142 of 239 cases).

Injury factors
For the pedestrian victims we first of all see that the head injuries are generally the
most common type of injury (44%), closely followed by injuries to the lower
extremities (38.8%), and then – but much less frequent - by injuries to the thorax
(8%; see also Figure 9).
The Chi-square test for the cross-table of injury type by cluster number is significant
(Chi-square = 24.900, df = 12, p < 0.05), indicating that for pedestrians there is a
significant relationship between the variables injury type and cluster number. When
inspecting the injury types in the three separate clusters, we see the following
pattern:
 Injuries to the head are relatively larger in crashes on roads with higher speed
limits (>30 km/h);
 Injuries to the lower extremities on the other hand are relatively larger in
crashes on 30 km/h roads.
Sweden
Crash characteristics
The dataset comprises 1,034 severely injured pedestrians. Gender is fairly evenly
distributed with 52% female and 48% male (Figure 2). There are 3 distinct peaks in
the age distribution; around 20 years, 60 years and 80 years (Figure 3).
Pedestrian impacts with cars form the majority of cases (72%). The next largest share
is when pedestrian was hit by a large vehicle, 18% (Figure 4). As expected, the
proportion of crashes with two road users (pedestrian + crash opponent) is 90%.
Looking at the road type, 86% of the crashes resulting in a MAIS3+ pedestrian
casualty occur in urban areas (the same as for cyclists; Figure 5). The crash occurred
most frequently on a street section, 67% of cases, and at an intersection 24% of the
time (Figure 6).

October 2016

70

Study on Serious Road Traffic Injuries in the EU

There is clearly a higher proportion of crashes occurring in November and December
than other months of the year (Figure 7). There is also a clear rise in the crashes after
mid-day, with the greatest proportion being between 3 and 6 pm (Figure 8).
Crash scenarios
A first TwoStep cluster analysis was undertaken with the nominal variables Urban
Number_Road_Users Crash_opponent Location_junction Hour Month and Sex and the
continuous variable Age, a total of 8 input variables. This resulted in a 3 cluster
solution with an AIC of 16831.16 and a cluster quality labelled as poor. The variables
Crash_opponent, and sex have a predictor importance > 0.5.
A second TwoStep Cluster analysis used input variables from the first with a predictor
importance > 0.5, crash_opponent and sex. This also produced a 3 cluster solution
with an improved AIC of 1079.62 and a cluster quality labelled as Good. In this
solution crash_opponet has predictor importance 1 and sex has a value of 0.75. The 3
clusters are described in table X along with the injury type distribution for each
scenario. The total column in the injury type section refers to the injury distribution for
all MAIS3+ pedestrians irrespective of cluster membership.
Cluster analysis resulted in 3 scenarios for the MAIS3+ pedestrians based upon the
input variables crash_opponent and gender (those with sufficient predictor values).
These are described below by the most frequently occurring value of each variable
within each cluster (see also Table 10). The proportion of cluster members with the
exact combination of these most frequent values is also given.
 Female pedestrians in a collision with a car (379 of 379 cases);
 Male pedestrians in a collision with a car (363 of 363 cases);
 Female pedestrians in collision with a large vehicle (87 of 194 cases)
Table 10: Crash scenarios and injured body regions for the Swedish pedestrian data (STRADA).
Cluster nr.
N
Crash Opponent

Injury factors
The most common body region that is severely injured in pedestrians in Sweden are
the lower extremities (36%) and the head (26%). Also thorax injuries (14%) and
injuries to multiple body regions are common (13%; see also Figure 9).
A chi-square test of association has been performed on the 3 x 7 contingency table
generated from cluster number by injury type (2=33.039, df =12, p=0.001),
however there are some cells with an expected count < 5 and so the result is not
valid. Looking at the injury type within cluster, the following appears:
 Lower extremity injuries are most prevalent when the impact object is a car
(both male 37% and female 44%)

October 2016

71

Study on Serious Road Traffic Injuries in the EU

 Head injuries have the highest proportion in the circumstances where the
impact partner is most often a large vehicle.
 The chest injury type rate is also higher in the scenario where a pedestrian is
most of the times hit by a large vehicle, compared with impacts with cars.
 Multiple injury type is most common, almost 16%, for the male pedestrians in
a collision with a car.
IGLAD database
Crash characteristics
Of the 49 MAIS3+ pedestrians in the iGAD database, two third male and one third
female pedestrians are severely injured in a road crash (see Figure 2). The largest
numbers of injured pedestrians are among the younger elderly (55-64) (see Figure 3).
In 84% of the pedestrian crashes the first crash opponent is a car and in 10% it was a
heavy vehicle. In 90% of the pedestrian crashes one other road user is involved (see
Figure 4). Most of the crashes can be described by a manoeuvre (of the opponent
vehicle) while going ahead (and crossing pedestrian). In the majority of cases
inadequate information acquisition contributed to the crash.
Most pedestrian crashes leading to severe injury occur in urban areas (86%; see
Figure 5) and on dry road surface (73%). Nearly 70% of the crashes occurred in
daytime, mainly during commuter times in the morning and the (early) afternoon (see
Figure 8).
Crash scenarios
A first TwoStep Cluster analysis of the pedestrian data with 49 cases and the above
stated variables yields a 2 cluster solution with an AIC of 1164 and a cluster quality
labelled as fair (0.3).
The second round with the variables DayNight, CrashOpponent, Manoeuvre, Gender,
RoadType, CrashFactor, AgeGroup yields a 4 cluster solution with an AIC of 608 and a
cluster quality labelled as good (0.5).
The third analysis yields a 4 cluster solution with an AIC of 247 and a cluster quality
labelled as good (0.56) with the variables (predictor importances) are RoadType (1.0),
DayNight (0.78), Manoeuvre (0.7), CrashFactor (0.05). The clusters that were found
could best be summarised as follows (see Table 11):
 Daytime crashes of pedestrians in urban areas during a straight forward
manoeuvre and in a situation with inadequate information acquisition (19 of
25)
 Nighttime crashes of pedestrians in urban areas during a straight forward
manoeuvre and in a situation with inadequate information acquisition (8 of 9)
 Daytime crashes of pedestrians in urban areas during overtaking a stationary
vehicle and in a situation with inadequate information acquisition (2 of 8)
 Daytime crashes of pedestrians in rural areas during a straight forward
manoeuvre (4 of 7) and in a situation with inadequate information acquisition
(1 of 7)
Injury factors
Table 11 shows the frequencies of the MAIS3+ injured body region per scenario. For
the pedestrian casualties injuries of the lower extremities (41%) are most common
followed by the thorax injuries (39%) and head injuries (22%) and multiple severe
injuries (18%; see also Figure 9).

October 2016

72

Study on Serious Road Traffic Injuries in the EU

The Chi-square test for the cross table of injury type by scenario is not significant
(Chi-square=14.869 df=15, p<0.461), indicating that for pedestrians there is no
significant relationship between the injury type and the crash scenario.
Table 11: Crash scenarios and injured body regions for the IGLAD pedestrian data (IGLAD).
Cluster nr.

Figure 6: Road configuration where crashes occur in which pedestrians get severely injured in the Czech
Republic (CziDAS data), England (linked: STATS19-HES), and England (in-depth: RAIDS/OTS), Germany
(GIDAS data), the Netherlands (BRON-DHD) and Sweden (STRADA data). *A staggered junction is a junction
were the side roads are not opposite to each other.

Figure 8: Time period of the day during which pedestrians get severely injured in the Czech Republic
(CziDAS data), England (linked: STATS19-HES), France, Rhône region (Rhône trauma register data),
Germany (GIDAS data), the Netherlands (BRON-DHD), Sweden (STRADA data), and the European sample
from the IGLAD database.

Bicyclists
Czech Republic
Crash characteristics
The CzIDAS cyclist data include 7 seriously injured casualties. Note the weak
explanatory power due to the low number of cases.
Nearly equally shares of men and women were seriously injured as a cyclist (see
Figure 10) most of them in the age 55+ (see Figure 11). In half of the crashes a car or
truck was the first crash opponent (see Figure 12) and in 72% (5 cases) 2 road users
were involved. All the crashes occurred in dry conditions mostly on road sections
(Figure 14) and in urban areas (see Figure 13). The majority of the crashes happened
in the summer months (see Figure 15) during daytime (see Figure 16).
Crash scenarios
Due to the low number of cases (n = 7), no cluster analysis was performed.
Injury factors
The most common injuries for seriously injured cyclists in the CzIDAS data are head
injuries (3/7; see Table 12 and Figure 17), which occurred only in urban area
accidents with a speed limit of 50 kph.
Table 12: Injured body regions for the Czech Republic bicyclist data (CziDAS).
Injury type

Total

Head, Face,
Neck

3/7=
42.9%

Thorax

1/7 =
14.3%

Lower extr.

2/7=
28.6%

Whole surf. +
mult. Regions

1/7=
14.3%

England
Crash characteristics
STATS19-HES - The linked STAST19-HES dataset comprises 2,012 severely injured
cyclists. There are considerably more male MAIS3+ cyclists (83%) than female (17%)
(Figure 10). There is a high proportion of children among the casualties (7 to 15
years; Figure 11). Pillion cyclists (two riders on a bike) are not common in this
dataset, less than 1%.
The most common crash opponent is a car (68%). Combining impacts with a fixed
object, a pedestrian and those with no impact partner shows impacts with no road
user opponent to comprise a further 17% of the total (Figure 12). The vast majority of
crashes involved two road users, defined as the number of vehicles in the crash. The
proportion of ‘no impact partner’ crashes is higher than the proportion of one road
user crashes since two vehicles can be involved in an crash without making impact
(non-cyclist making avoidance manoeuvres etc).

October 2016

82

Study on Serious Road Traffic Injuries in the EU

For the cyclist data, just over half of the impacts are frontal, a third to the side and
10% to the rear. In over 80% of the cases, a forward manoeuvre without turning or
overtaking precedes the crash.
Road type (Urban/Rural/Motorway) has been derived from the road classification and
speed limit in this dataset and therefore the distribution is approximate. The indication
is that the vast majority (84%) of the crashes with MAIS3+ cyclists occur in an urban
environment (Figure 13). Considering any junction layout, almost 40% of the crashes
occur at T/Y or staggered junctions with the next most common scenario being not at
a junction, i.e. on a road section (Figure 14). The road surface was dry in almost 80%
of cases.
Considering the date and time of the crash, there is a rise in the number of crashes as
the weather improves into spring and this then peaks across summer (Figure 15).
There is a higher frequency of crashes during mid to late afternoon (Figure 16).
Most common crash factors are:
 Failure to look properly (55%)
 Careless / reckless behaviour (19%)
 Failure to judge path / speed of other road user (19%)
 Loss of control (12%)
 Poor turn / manoeuvre (12%)
RAIDS/OTS - The combined RAIDS/OTS dataset comprises 18 severely injured car
occupants of which 94% are male and just 6% (one) female (Figure 10). Age data is
only available for the RAIDS data (n=2). All of the cyclists were ‘drivers’. In respect of
seating position, 63% are drivers, 21% front seat passengers and 16% seated in the
rear.
The most common crash opponent is another car (78%) and the number of road users
shows 88% of cases with two road users involved. Considering the location of the
impact on the bike; the majority of cyclists resulting in MAIS3+ injury have an impact
to the side (56%) followed by the front (33%). In 83% of the cases, cyclists was
moving forward without turning or overtaking and was turning in the remaining 17%.
Two thirds of the crashes occur in urban areas and one third in rural (Figure 13). The
speed limit is 40mile/h or less in 83% of cases. Over half of the crash occurred at a T
/ Y or staggered junction (56%) and 33% were only a simple road section (Figure 14).
The road surface was dry in 78% of cases.
Month and time are not available in England in-depth databases for privacy protection
and so daytime / night-time has been used as a substitute. 83% of the severe injury
crashes occur during the daytime, with 17% at night time.
The most common factors that were found to play a role in the crash were failure to
look properly (39%), careless/reckless behaviour (28%) and vision affected (22%).
Crash scenarios
STATS19-HES - A first TwoStep Cluster analysis of the Cyclist data with 2,012 cases
was undertaken using the nominal variables Casualtyrole Month, Time, opponent,
CrashType, Gender, Junction, Surface, Manoeuvre ActiveRoadUsers and SpeedLimit
and the interval variable Age, a total of 12 input variables. This resulted in a 5 cluster
solution with an AIC of 260780.4 and a cluster quality labelled as Poor.

October 2016

83

Study on Serious Road Traffic Injuries in the EU

A second TwoStep Cluster analysis used input variables from the first with a predictor
importance > 0.5, SpeedLimit and Surface only. A 3 cluster solution was returned with
an AIC of 3563.425 and a cluster quality labelled as Good. In this solution the
variables SpeedLimit and Surface both have a predictor importance = 1.0. The 3
clusters are described in Table 13 in order of cluster size. The clusters could best be
summarised as:
 Bicyclists crashes at a road with a 30 mile/h speed limit under dry conditions
(1208 of 1208 cases);
 Bicyclist crashes at a 30 mile/h road in wet conditions (282 of 423 cases);
 Bicyclists crashes at a 60 mile/h speed limit road in in dry conditions (192 of
381 cases).
The first two are likely to be more urban whilst the third more rural.
Failure to look properly is the most reported factor, over 50% in all clusters. Failure to
judge the path or speed of another road user, loss of control and careless/reckless
behaviour also feature in all clusters. Loss of control features more in the higher speed
limit cluster. Poor turn or manoeuvre are reported in the 5 most frequent factors for
the dry conditions clusters whereas the road condition and the cyclist clothing are
reported when the road surface was wet.
Table 13: Crash scenarios for England bicyclist data (STATS19-HES).
Cluster nr.
N
Speed Limit

1
1208
30 mile/h (100%)

2
423
30 mile/h (70%)

3
381
60 mile/h (50%)

Surface

Dry (100%)

Wet (95%)

Dry (100%)

RAIDS/OTS - A first TwoStep Cluster analysis of the cyclist data with 18 cases was
undertaken using the nominal variables DayNight, opponent, Gender, Junction,
Surface, Manoeuvre, ActiveRoadUsers, roadtype, impact loaction and speed a total of
10 input variables. This resulted in a 2 cluster solution with an AIC of 237.592 and a
cluster quality labelled as Fair. Roadtype, DayNight and Manoeuvre had predictor
importance > 0.5 and these were entered into a further cluster analysis.
The second analysis using, the 3 input variables identified in the first, resulted in 3
cluster solution with AIC 30.14 labelled good, however only roadtype and manoeuvre
have importance > 0.5. These 2 variables were included in a further cluster analysis.
The 2 input variables roadtype and manoeuvre produced a 3 cluster solution with AIC
15.82 with both manoeuvre having importance 1.0 and roadtype 0.83. The clusters
are described as (see Table 14):
 Cyclists going ahead in an urban area (11 of 11 cases)
 Cyclist going ahead in rural area (4 of 4 cases)
 Cyclist turning in rural area (2 of 3 cases)
Injury factors
The Table shows the injury distribution by body region for MAIS3+ bicyclists in the
RAIDS/OTS data. The lower extremities have the highest proportion (39%) in severely
injured bicyclists, followed by the head (22% each) and thorax (17%; see also Figure
17).
As the RAIDS/OTS data contained too small number of cases to analyse them in a
cluster analysis, the injury types could not be linked to crash scenarios. However there
are some interesting qualitative observations:

October 2016

84

Study on Serious Road Traffic Injuries in the EU





Chest injury type, (most severe of all injuries) only occurs in the urban areas
and they also only occur at junction crashes.
Half of the urban impacts resulted in the most severe injury to the lower
extremity compared to 1/6 in the rural area.
When the impact location was to the font/rear, the most severely injured body
region tends to be the lower extremity (5/8), but when it is a side impact head
and chest injury type feature more.

France, Rhône region
Crash characteristics
The bicycle data of the Rhône area contained 594 cases. The data showed that
particularly male bicyclists are severely injured (78%; see Figure 10). Middle aged and
younger elderly people (45 to 64 years) are dominating the number of serious
pedestrian injuries (see Figure 11). By far most bicyclists get severely injured in an
crash without any other road user (64%). Cars are a second most important crash
opponent (19%; see Figure 12). In the Rhône area, 28% of the bicyclists wear a
helmet. Most of the severe cycling crashes (ca. 75%) occur during daytime (see
Figure 16).
Crash scenarios
A first TwoStep Cluster analysis of the nominal variables DayNight,
FirstCrashOpponent, DemograpyAgeGroup, DemographyGender and Helmet use yields
a 2 cluster solution with an AIC of 5109.749 and a cluster quality labelled as Fair. In
this solution the variable Helmet use has a predictor importance of 1.0, while the
predictor importance of the remaining four variables is almost zero. This means that
the variable Helmet use completely dominates the cluster solution, swamping out all
the other variables.
A second TwoStep Cluster analysis without variable Helmet use yields a 4 cluster
solution with an AIC of 3646.925 and a cluster quality labelled as Fair. In this solution
the
variables
DayNight,
FirstCrashOpponent,
DemograpyAgeGroup
and
DemographyGender have a predictor importance of 1.00, 0.73, 0.40 and 0.10,
respectively.
So
we
also
dropped
variables
DemograpyAgeGroup
and
DemographyGender from the cluster analysis. This resulted in a solution with 4
clusters with an AIC of 565.274 and a cluster quality labelled as Good. The variables
FirstCrashOpponent and DayNight in this solution have a predictor importance of 1.0
and 0.67, respectively.
The clusters that were found can best be described as follows (see Table 15):

October 2016

85

Study on Serious Road Traffic Injuries in the EU






Bicyclist gets severely injured in an crash with no crash opponent during
daylight (332 of 332 cases);
Bicyclist gets severely injured in an crash with no crash opponent during night
time (50 of 92 cases);
Bicyclist is hit by a car during daylight (90 of 90 cases);
Bicyclist that hits a fixed object during daylight (35 of 80 cases).

Injury factors
The lower part of the Table displays the frequencies in the cross-table of injury type
by cluster number. The most common injury types of severely injured bicyclists in the
Rhône area are injuries to the upper extremities (40,4%), followed by the lower
extremities (30,5%) and injuries to the head, face and neck (21%; see Figure 17).
Since there are only two Face and two Neck injuries in this data set we added these
four cases to the Head injuries. Moreover, there are only 14 injuries of the Abdomen
and pelvic contents in this data set, only 19 Spine injuries, and only 27 multiple region
injuries. We therefore dropped these injury types from the analysis because they
result in 12 cells in the cross-table with expected frequencies smaller than 5. The Chisquare test for the cross-table of the remaining four categories of injury type by
cluster number is very significant (Chi-square = 36.996, df = 9, p < 0.0001),
indicating that for cyclists there is a significant relationship between the variables
injury type and cluster number. The most important differences are:
 Injuries to the upper extremities are most common in crashes where a
bicyclists get injured without a crash opponent and also common in crashes
where the bicyclists hits a fixed object.
 Injuries to the lower extremities are most common in the crash scenarios
where a bicyclists gets injured without impact with a crash opponent during
daytime and when hit by a car.
 Injuries to the head, face and neck are most common in crashes where the
bicyclists hit a fixed object (equal injury share as injuries to the upper
extremities in this scenario) and also quite common when hit by a car or in an
crash without crash opponent during nighttime.
Germany
Crash characteristics
In total 245 MAIS3+ bicyclist casualties were found in GIDAS and included in the
analysis. About twice as much males than females got severely injured in a bicycle

October 2016

86

Study on Serious Road Traffic Injuries in the EU

crash (see Figure 10). The largest numbers of injured bicyclists are found for the midagers starting at 35 and the young seniors (see Figure 11).
In about half of the bicycle crashes the first crash opponent is a car (111 cases).
About equally often a single crash or a crash with a fixed object occur for cyclists.
8% of the crashes occur with heavy vehicles (bus, truck, rail, agricultural vehicles). In
most bicycle crashes one other road user is involved, but in about 23% the crash is a
single crash (see Figure 12).
Most bicycle crashes leading to severe injury occur on urban 50 or 70 km/h roads (see
Figure 13). About 60% of the bicycle crashes resulting in severe injury occur at
junctions (see Figure 14) and about 80% on dry road surface. 38% of the cyclists
become injured in an turning crash, 58% in an crash that is characterized as going
ahead round curve/other which includes also crashes of a car going ahead and a
crossing cyclist. The majority of crash occur at no special location, 18 % at VRU
crossings and 90% of the cases are found in not physically divided two-way traffic.
Regarding the crash factor in 75% of the cases inadequate information acquisition of
one of the participants contributed to the crash and in 9 % it was speeding (probably
of the opponent) and red light running (7%). Regarding the injury factors 95 % of the
severely injured cyclists did not wear a helmet and the other 5% did not use the
helmet properly.
During spring and summer (May and August), the highest number of bicyclists get
severely injured (see Figure 15). Most crashes occur in the afternoon between 3:00
PM and 6:00 PM (see Figure 16).
Crash scenarios
A first TwoStep Cluster analysis of the cyclist data with 245 cases and the above
stated variables yields a 5 cluster solution with an AIC of 6287 and a cluster quality
labelled as poor (0.1). In this solution only the variables JunctionType, Location,
CrashFactor, Manoevre, Gender, CrashOpponent, Time, have a predictor importance
larger than 0.2.
A second TwoStep Cluster analysis with these yields a 4 cluster solution with an AIC of
3887 and a cluster quality labelled as fair (0.2). This solution suggests the removal of
the variables Gender, Time, Manoevre, JunctionType.
A third TwoStep Cluster analysis yields a 4 cluster solution with an AIC of 925 and a
cluster quality labelled good (0.6) with the variables (predictor importance) Location
(1.0), CrashFactor (0.77), and CrashOpponent (0.67). The resulting clusters are
described in Table 16 and can best be summarised as:
 Bicyclist in a single crash in a situation with inadequate information acquisition
and no specific infrastructural situation (56 of 81 cases);
 Bicyclist against car in a situation with inadequate information acquisition and
no specific infrastructural situation (63 of 65 cases);
 Bicyclist against a car, at a VRU crossing facility in a situation with inadequate
information acquisition (10 of 61 cases);
 Bicyclist against a car in a situation where the car was speeding (6 of 38
cases).

Injury factors
Table 16 shows the most common crash scenarios including the frequencies of the
body regions that were MAIS3+ injured per scenario. For the cyclist casualties injuries
to the head are most common (36%) followed by injuries to the lower extremities
(33%) and injuries of the thorax (18%; see Figure 17).
The Chi-square test for the cross table of injury type by scenario is not significant
(Chi-square=22.963 df=18, p<0.192), indicating that for cyclists there is no
significant relationship between the injury type and the crash scenario.
Having a closer look into the injury distribution within the 4 scenarios shows: single
vehicle crashes are characterized by head injuries (43%) and injuries of the lower
extremities (38%). Most casualties are mid-agers (25% 45-54 years) or seniors (22%
65-74 years).
For the 245 cyclists casualties 1333 single injuries (mean of 6 injuries per casualty)
were recorded of which 382 injuries (mean of 2 severe injuries per casualty) have a
severity of AIS08=3 or larger. The majority of fractures were caused by the contact
with the opponent car front (56/179=65.9%) or the contact with the road and
environmental features (e.g. the curb) (118/179=65.9%). These causes are also the

October 2016

88

Study on Serious Road Traffic Injuries in the EU

main causes for all injury types, that is 220/382=57.6% of all injuries were caused by
hitting the road, 136/382=35.6% were caused by the impact to the opponent which
was in most cases a car.
Netherlands
Crash characteristics
BRON-LMR - The analysis was performed on the BRON-DHD linked police data of
6,902 cases in which a bicyclist was severely injured. Somewhat more males than
females get severely injured in bicycle crashes (see Figure 10). The largest numbers
of injured bicyclists are among adolescents (12 to 17 years of age), middle aged and
elderly people (50 to 80 years; Figure 11).
Somewhat more than half of the bicycle crashes are with a car (3,776 cases). Other
common crash opponents are bicyclists, powered two wheelers, vans and trucks. Only
7% of the crashes that are registered by the police are single vehicle crashes (see
Figure 12), but from the DHD traffic register we know that this share is much larger.
In most bicycle crashes in the BRON-DHD database one other road user is involved.
About two third of the crashes are a side-impact crash and also head-on collisions are
quite common.
Most bicycle crashes leading to severe injury occur on urban 50 km/h roads (see
Figure 13). About 60% of the bicycle crashes resulting in severe injury occur at
junctions (see Figure 14) and about 80% on dry road surface.
During spring time and early summer (March to June) and September, the highest
number of bicyclists get severely injured (see Figure 15). Most crashes occur in the
afternoon between 3:00 PM and 6:00 PM (see Figure 16).
DHD traffic register - For the bicyclists, also the DHD hospital discharge was analysed
since it is known that for this traffic mode there is a bias in the BRON linked to DHD
statistics towards crashes including motorised vehicles, leaving single bicycle crashes
largely underreported. The DHD traffic register contained 26,335 MAIS3+ injured
bicyclists that were analysed.
Analysis of this data shows a nearly equal share of males and females (52% versus
48%). The DHD traffic register data show particularly large numbers of elderly
bicyclists (60 years and older). According to the DHD traffic register data, only 16% of
the bicyclist crashes involves a motor vehicle (see Figure 12), which means that the
majority of the severe bicycle crashes is a single bicycle crash or with a non-motorised
vehicle (e.g. pedestrian or another cyclist). Months with larger frequencies of serious
injured bicyclists than others are May to September. Most serious bicyclist crashes
occur in the afternoon.
Crash scenarios
When performing the TwoStep cluster analyses on the BRON-DHD data and the
nominal variables Month, Time, Crash opponent, Crash type, Gender, Road type,
Junction, Road surface, and Speed limit and the interval variables Number of active
road users and Age yields a 5 cluster solution with an AIC of 157763.703 and a
Cluster Quality labelled as Poor. In this solution all the variables have a predictor
importance of 1.0 except for Month, Time and Gender.
A second TwoStep Cluster analysis without the latter three variables yields a 2 cluster
solution with an AIC of 91786.826 and a Cluster Quality labelled as Fair. In this
solution the variables Junction, Crash type, Number of active road users, Road surface
and Age have a predictor importance smaller than 0.2.

October 2016

89

Study on Serious Road Traffic Injuries in the EU

A third TwoStep Cluster analysis also removing the latter five variables yields a 6
cluster solution with an AIC of 39466.078 and a Cluster Quality also labelled as Fair.
In this solution all four remaining variables have a perfect predictor importance of 1.0.
These 6 clusters are described in Table 17 in order of cluster size.
The six scenarios that appear to be most common for bicyclists that got severe
injuries in the Netherlands can be described as follows (see Table 17):
 Side-impact crash during turning manoeuvre with a car as crash opponent on
an urban 50 km/h road (1061 of 1682 cases);
 Head-on collision with a car on an urban 50km/h road (409 of 1424 cases);
 Side-impact crash during turning manoeuvre, with a car as crash opponent on
a rural 80 km/h road (213 of 1328 cases);
 Side-impact crash during turning manoeuvre with another bicycle as crash
opponent on an urban 50 km/h road (157 of 1114 cases);
 Side-impact crash during turning manoeuvre, with a car as crash opponent on
an urban 30 km/h road (119 of 883 cases);
 Single vehicle-crash on an urban 50km/h road (162 of 471 cases).
Table 17: Crash scenarios and injured body regions for the Dutch bicyclist data (BRON-DHD).
Cluster
nr.
N
Crash type

Injury factors
For the bicyclist victims we first of all see that the head and face injuries are generally
the most common type of injury (51%), followed by injuries to the lower extremities
(31%), and then by injuries to the thorax (11%; see Figure 17).
The Chi-square test for the cross-table of injury type by scenario is very significant
(Chi-square = 177.884, df = 30, p < 0.001), indicating that for bicycles there is a
significant relationship between the injury type and crash scenario.
The most remarkable differences are:
 Head injuries are the most common injury type for all crash scenarios, but
somewhat less in single bicycle crashes where hip and pelvis fractures are most
dominant.
 Bicyclists sustain more thorax injuries in impacts with a car, and more at roads
with a higher speed limit, independent of the manoeuvre that precedes the
crash.
 Injuries to the lower extremities are particularly common in single-bicycle
crashes and in crashes with another bicycle.
Sweden
Crash characteristics
The dataset comprises 1,044 severely injured cyclists of which 58% are male and 42%
female (Figure 10). There are distinct peaks in the age distribution for children around
14 years old, and adults in their mid-fifties (Figure 11). Only 3 of the 1,044 cyclists
are coded as being a rear passenger.
Bicycle to car impacts are the most common crash scenario (61%). The next largest
share is for the single vehicle crashes (17%; Figure 12). This is supported by the
number of road users which shows 16% of cases with just the cyclist involved (81%
with 2 road users).
Looking at the road type, 86% of the crashes resulting in a MAIS3+ cyclist occur in
urban areas (Figure 13). The crash occurred at an intersection (junction) in 44% of
cases, and on a road section 35% of the time. In 17% of the cases the data is coded
as a walkway or cycle lane (Figure 14).
The month with the highest proportion of crashes is August with generally more
crashes occurring May – October than over the winter months (Figure 15). The vast
majority of crash occur between 6am and 6pm, with the greatest proportion being
between 3 and 6 pm (Figure 16).
Crash scenarios
A first TwoStep cluster analysis was undertaken with the nominal variables Urban
Number_Road_Users Crash_opponent Location_junction Hour Month and Sex and the
continuous variable Age, a total of 8 input variables. This resulted in a 5 cluster
solution with an AIC of 16694.34 and a cluster quality labelled as Fair. The variables
Crash_opponent, Number_road_users and Location_junction have a predictor
importance > 0.5.
A second TwoStep Cluster analysis used input variables from the first with a predictor
importance > 0.5, Number_road_users, Crash_opponent and location_junction. This
produced a 5 cluster solution with an improved AIC of 2270.110 and a cluster quality
labelled as Good. In this solution the 3 input variables all have predictor importance >
0.7. Removing Number_Road_Users, lowest predictor importance, produced a 3
cluster solution but with an increased AIC.

October 2016

91

Study on Serious Road Traffic Injuries in the EU

Thus, the second solution was chosen. The 5 clusters are described in Table 18 along
with the injury type distribution for each scenario. They can be summarised as follows
(see Table 23):
 Cyclist to car crashes at intersections with 2 road users (329 of 329 cases);
 Cyclist to car crashes on a street section with 2 road users (198 of 201 cases);
 Crashes with only the cyclist involved (single vehicle)* on a street section, one
road user (72 of 194 cases);
 Cyclist to car impacts on a walkway or cycle path involving 2 road users (30 of
172 cases);
 Crashes involving the cyclist and predominantly a large vehicle, at intersections
with 2 road users (51 of 148 cases).
Table 18: Crash scenarios and injured body regions for the Swedish bicyclist data (STRADA).
Cluster nr.

1

3

4

2

5

N

329 (31.5%)

201 (19.3%)

194 (18.6%)

172 (16.5%)

148 (14.2%)

Crash
Opponent

Car (100%)

Car (98.5%)

Single Vehicle
(76.8%)

Car
(42.4%)

Junction/
road section

Intersection
(100%)

Street Section
(100%)

Street Section
(51%)

Number
road users
Injury type

2 (100%)

2 (100%)

1 (86.6%)

Walkway /
Cycle path
(71.5%)
2 (100%)

Large
Vehicle
(51.4%)
Intersection
(59.5%)

Head, Face,
Neck

34.3%

39.3%

51.5%

34.3%

31.8%

38.1%

Thorax

17.6%

17.4%

10.8%

18.0%

10.1%

15.3%

Abdomen
and pelvic
cont.s
Spine

1.2%

1.5%

2.1%

0.6%

0.7%

1.2%

3.0%

3.0%

5.7%

4.1%

2.0%

3.5%

Upper extr.

6.1%

2.0%

8.8%

6.4%

10.8%

6.5%

Lower extr.

28.0%

24.4%

17.5%

31.4%

30.4%

26.2%

Whole surf.
+ mult.
regions

9.7%

12.4%

3.6%

5.2%

14.2%

9.0%

2 (100%)
Total

Injury factors
Bicyclists in Sweden appear to sustain most severe injuries to the head (38%),
followed by the lower extremities (26%) and the thorax (15%; see also Figure 17). A
chi-square test of association has been performed on the 5 x 7 contingency table
generated from cluster number by injury type (2=63.837, df =24, p<0.001),
however there are some cells with an expected count < 5 and so the result is not
valid. A further chi-square was performed looking at overall injury severity between
the clusters. A visual examination of the cross-tabulation shows that:
 Head injuries have the highest proportion across all scenarios with the highest
being in single bicycle crashes
 Cyclists that are hit by a heavy vehicle, have the highest rate of multiple region
injuries.

October 2016

92

Study on Serious Road Traffic Injuries in the EU

IGLAD database
Crash characteristics
In total 17 MAIS3+ cyclist casualties were found in IGLAD database and were included
in the analysis. The number of casualties is very low and must not be considered as
representative.
Two third of the MAIS3+ cyclists are male (see Figure 10). Due to the low number of
casualties no particular age group can be identified (see Figure 11). In two third of the
crashes the crash opponent is a car and in 80% two road users were involved (see
Figure 12). The cyclists were hurt mainly in crashes that were preceded by a turning
manoeuvre (35%) or going straight (41%). The most important contributing crash
factor was inadequate information acquisition (88%). In nearly 90% the road
conditions were dry and crashes occurred between 9:00 AM and 8:00 PM (see Figure
16).
Table 19: Crash scenarios and injured body regions for the IGLAD bicyclist data (IGLAD).
Cluster nr.

1

2

N

14

3

Active Road Users

2
14/14
=100%

1
3/3
=100%

Crash Opponent

Car
10/14
=71%

No opponent
3/3
=100%

Crash Factor

Inadequate
acquisition
14/14
=100%

Manoeuvre

turning
6/14
=43%

information

Inadequate
acquisition
1/3
=33%

information

Going ahead other
2/3
=67%

Injury Type

Total

Head, Face, Neck

5/14
=35.7%

2/3
=66.7%

7/17
=41.2%

Thorax

3/14
=21.4%

0/3
=0%

3/17
=17.6%

Abdomen and pelvic
cont.s

0/14
=0%

0/3
=0%

0/17
=0%

Spine

1/14
=7.1%

0/3
=0%

1/17
=5.9%

Upper extr.

0/14
=0%

0/3
=0%

0/17
=0%

Lower extr.

3/14
=21.4%

1/3
=33.3%

4/17
=23.5%

Whole surf. + mult.
Regions

2/14
=14.3%

0/3
=0%

2/17
=11.8%

Crash scenarios
A first TwoStep Cluster analysis of the cyclist data with 17 cases and the above stated
variables yields a 2 cluster solution with an AIC of 286 and a cluster quality labelled as

October 2016

93

Study on Serious Road Traffic Injuries in the EU

fair (0.3). The model suggests the removal of the variables Gender, Surface, Age,
RoadType (predictor importance <0.2).
A second TwoStep Cluster analysis of the cyclist data with Time, CrashOpponent,
ActiveRoadUsers, Manoevre, CrashFactor yields a 2 cluster solution with an AIC of 208
and a cluster quality labelled as quite fair (0.5).
A third TwoStep Cluster analysis of the cyclist data with CrashOpponent,
ActiveRoadUsers, Manoevre, CrashFactor yields a 2 cluster solution with an AIC of 105
and a cluster quality labelled as good (0.6). The variables (predictor importances) are
AcvtiveRoadUsers (1.0), CrashOpponent (0.61), CrashFactor (0.52) and Manoevre
(0.27). The clusters that were found can best be summarized as follows (see Table
19):
 Bicyclist against a car while turning in a situation with to inadequate data
acquisition (5 of 14)
 Single bicyclist crash while going ahead (2 of 3) in a situation with inadequate
data acquisition (2 of 3).
Injury factors
Table 19 shows the frequencies of the MAIS3+ injured body regions per scenario. For
the cyclist casualties head injuries are most common (41%) followed by injuries of the
lower extremities (24%) and the thorax (18%; see also Figure 17).
The Chi-square test for the cross table of injury type by scenario is not significant
(Chi-square=2.009 df=4, p<0.734), indicating that for cyclists there is no significant
relationship between the injury type and the crash scenario.

17%
Figure 14: Road configuration where crashes occur in which bicyclists get severely injured in the Czech
Republic (CziDAS data), England (linked: STATS19-HES), and England (in-depth: RAIDS/OTS), Germany
(GIDAS data), the Netherlands (BRON-DHD) and Sweden (STRADA data). *A staggered junction is a junction
were the side roads are not opposite to each other.

Figure 16: Time period of the day during which bicyclists get severely injured in the Czech Republic (CziDAS
data), England (linked: STATS19-HES), France, Rhône region (Rhône trauma register data), Germany
(GIDAS data), the Netherlands (BRON-DHD), Sweden (STRADA data), and the European sample from the
IGLAD database.

Motorcyclists
Czech Republic
Crash characteristics
The CzIDAS PTW data include 33 seriously injured casualties. The group includes 1
moped, 3 PTW of unknown type and 29 motorcyclists, thus, the groups are merged
into one.
¾ of the seriously injured PTW riders were male (see Figure 18) and 91% were the
driver of the vehicle. The most prominent group of injured PTW riders is found in the
young(er) drivers (17-34 years; see Figure 19).
In 21% of the crashes only one road user was involved, in 43 one other road user was
involved and in 36% three or more road users were involved. In nearly equal shares
the PTW rider’s first crash opponent was a car or a fixed object/ single crash (see
Figure 20).
In 37% of the cases, the PTW riders were injured in an crash that was characterized
as a turning crash. In each 27% of the cases the manoeuvre was going ahead round
curve or going ahead other. In 43% of cases the contributing crash factor was
inadequate information acquisition of one of the crash participants in 21% one of the
drivers was under influence. In each 15% of the cases speeding or tailgating
contributed to the crash.
Two third of the crashes occurred on road sections (see Figure 22) and nearly half of
the PTW rider were injured in urban areas (see Figure 21). 36% of the casualties were
injured on rural roads with a speed limit of 80 or 90 km/h. The crashes happened
mostly in not physically divided two-way traffic (94%) and in the majority of cases in
no special location (85%).
The majority of crashes occurred under dry conditions (88%) in the morning and early
afternoon (see Figure 24). Most crashes occurred during the summer months (JulySeptember; see Figure 24).
Crash scenarios
A first TwoStep Cluster analysis of the pedestrian data with 26 cases and the above
stated variables yields a 2 cluster solution with an AIC of 1 403 and a cluster quality
labelled as fair (0.3). The next iteration was done with the variables Month,
CrashOpponent, Manoeuvre, RoadType, SpeedLimitJunctionType, CrashFactor.
The second round yields a 2 cluster solution with an AIC of 572 and a cluster quality
labelled as fair (0.4).
The third round yields a 3 cluster solution with an AIC of 223 and a cluster quality
labelled as good (0.5). The variables (predictor importance) are Manoeuvre (1.0),
JunctionType (0.91), RoadType (0.74), SpeedLimit (0.61). This cluster result (see
Table 20) can best be summarised as:
 PTW on rural road section with a speed limit of 90 kph going straight (9 of 19);
This are mainly single vehicle crashes (63%);
 PTW at urban junction with a speed limit of 50 kph in turning manoeuvre (7 of
14). In this scenario, the crash opponent is mainly a car (64%).

Injury factors
Most PTW serious injuries were found on the lower extremities (39%), followed by the
head and multiple regions (18%; see Figure 25).
The Chi-square test for the cross table of injury type by scenario is not significant
(Chi-square =8.016, df =5, p<0.155), indicating that for PTW rider there is no
significant relationship between the injury type and the crash scenario.
England
Crash characteristics
STATS19-HES - The STAST19-HES linked dataset comprises 5,424 severely injured
motorcyclists. The vast majority of these, 95%, are male (Figure 18). Those aged 17
and 18 occur most frequently in the data, with another small peak at age 40 (Figure
19) with the mean age being 33 (SD 13). Of the MAIS3+ non-fatal motorcyclists, 95%
are drivers and 5% passengers.
The most common crash opponent is another car (59%). Combining impacts with a
fixed object (17%) and those with no impact partner (12%) shows impacts with no
road user opponent to comprise 39% of the total (Figure 20). Two thirds of the
crashes resulting in MAIS3+ motorcyclist involved two road users, with a little of 20%
involving just the motorcyclist. In ¾ of cases the location of the impact on the

October 2016

104

Study on Serious Road Traffic Injuries in the EU

motorcycle is to the front with a further 21% to the side. In over 80% of the cases,
the motorcycle is moving forward without turning or overtaking.
Road type (Urban/Rural/Motorway) has been derived from the road classification and
speed limit in this dataset and therefore the distribution is approximate. It is
suggested that 60% of the crashes with MAIS3+ car motorcyclists occur in an urban
environment, 38% in rural areas and just 2% on motorways (Figure 21). Looking at
any junction layout, almost 40% of the crashes occur on a road section (no junction).
Crashes at T/Y or staggered junctions are almost as frequent 35% (Figure 22). The
road surface was dry in 81% of cases.
There is a rise in the frequency of crashes during the summer months, peaking in
August (Figure 23) and there is a higher frequency of crashes from mid-afternoon
though to 9pm (Figure 24
The most common crash factors are;
 Failure to look properly (40%)
 Speeding or inappropriate speed for conditions (26%)
 Loss of control (25%)
 Poor turn / manoeuvre (25%)
 Failed to judge path or speed of other road user (23%)
 Careless/reckless behaviour (23%).
RAIDS/OTS - The combined RAIDS/OTS dataset comprises 67 severely injured
motorcyclists. The vast majority are male (91%) (Figure 18). Age data is only
available for the RAIDS data (n=16) – in this small sample there is an even
distribution casualties across all ages. The casualty was the driver 97% of the time.
The most common crash opponent is another car (61%). The next most frequent
opponent is a fixed object, 21% (Figure 20). Unlike car impacts where the majority of
fixed object impacts are trees and road side furniture, for motorcyclists these tend to
be kerb strikes resulting in sliding across the ground. Looking at the number of road
users, this supports the crash opponent result; 27% have one road user which
equates to the fixed and no impact within the crash opponent. In 57% of cases there
were two road users. Whilst the majority of impacts are frontal (46%), there are a
high proportion classified at skidding (27%) – this varies from the national data and is
explained by the coding options in the different data sets. In over ¾ of cases the
motorcycle was simply going forwards, and in a further 15% there was a manoeuvre
that involved moving out of lane.
Considering the road type (Urban/Rural/Motorway) there is an even split of urban and
rural crashes (Figure 21). This is also reflected in the speed limit distribution where a
combination of 30 and 40 mile/h account for 55% of the crashes. The road layout
shows that almost half of the crashes resulting in severe motorcycle crashes occurred
at a T or staggered junction with almost as many occurring on a simple road section
(see Figure 22). The road surface was dry in almost ¾ of cases.
Considering the lighting conditions, ¾ of the crashes occurred during the daytime.
The most common crash factors that were found are:
 Careless / reckless behaviour (43%);
 Speed as a factor (34%);
 Vision affected (34%);
 Poor turn or manoeuvre (31%);
 Loss of control (25%).

October 2016

105

Study on Serious Road Traffic Injuries in the EU

Crash scenarios
STAST19-HES - A first TwoStep Cluster analysis of the Motorcylist data with 5,424
cases was undertaken using the nominal variables Casualtyrole Month, Time,
opponent, CrashType, Gender, Junction, Surface, Manoeuvre ActiveRoadUsers and
SpeedLimit and the interval variable Age, a total of 12 input variables. This resulted in
a 2 cluster solution with an AIC of 134371.8 and a cluster quality labelled as Fair.
A second TwoStep Cluster analysis used the 4 input variables from the first with a
predictor importance > 0.5 (Opponent, Manouevre, Junction and ActiveRoadUsers). A
2 cluster solution was returned with an AIC of 41653.7 and a cluster quality labelled
as Fair. In this solution Opponent and ActiveRoadUsers have predictor importance of
1, Manoeuvre 0.46 and Junction 0.42.
Removing the latter 2 variables produces a 4 cluster solution based upon the two
variables opponent and ActiveRoadUsers. This has an AIC of 7437.2 and Good cluster
quality. This solution (see Table 21) can be described as:
 Motorcyclists crashes with a car (2775 of 2775 cases);
 Motorcyclist crashes with a fixed object (789 of 1207 cases);
 Motorcyclist crashes with a van (237 of 834 cases);
 Motorcyclist crashes with a car and at least one other road user is involved as
well (339 of 608 cases).
When the crash involves in main an impact with another road user the same five
factors are most common: failure to look, judgement of other vehicles path/speed,
speeding/inappropriate speed, careless/reckless behaviour, poor turn or manoeuvre.
However, in impacts with fixed object, loss of control is the most dominant crash
factor, and the driver being under the influence of drugs or alcohol also features.
Table 21: Crash scenarios for England motorcyclist data (STATS19-HES).
Cluster nr.
N
Number
Active
Road Users
Opponent

1
2747
2 (100%)

4
1207
1 (100%)

Car (100%)

Fixed
(69%)

Object

3
862
2 (100%)

2
608
3 (79%)

Van (28%)

Car (67%)

RAIDS/OTS- A first TwoStep Cluster analysis of the Motorcyclist data with 67 cases
was undertaken using the nominal variables DayNight, opponent, Gender, Junction,
Surface, Manoeuvre, ActiveRoadUsers, roadtype and speed a total of 9 input variables.
This resulted in a 5 cluster solution with an AIC of 877.63 and a cluster quality labelled
as Fair.
A second TwoStep Cluster analysis used input variables from the first with a predictor
importance > 0.5 (Roadtype speed opponent and surface). In this case a 2 cluster
solution was returned with an AIC of 354.88 and labelled good. Just 2 of the input
variables had predictor importance > 05, RoadType and speed.
These latter two were entered into a final analysis which produce a 2 cluster solution
(Good, AIC 124.41) categorised as Urban crash, mostly 30 mile/h rural crashes,
mostly 60 mile/h. The details are shown in Table 22 and can best be summarised as:
 Motorcyclists crashes on an urban road where the speed limit is typically
30mile/h (27 of 34 cases);
 Motorcyclist crashes on a rural road with a typical speed limit of 60mile/h (20
of 33 cases).

October 2016

106

Study on Serious Road Traffic Injuries in the EU

When the motorcyclist accidents are split into these two clusters, whilst there are
many factors in common, there are some indications that the contributory factors
differ: Crashes inside urban areas features failure to look unlike the rural accidents,
whereas loss of control and swerving are more common in in the scenario with rural
crashes.
Table 22: Crash scenarios and injured body regions for England motorcyclist data (RIADS & OTS).
Cluster nr.
N
Road type
Speed limit
Injury type
Head, Face, Neck
Thorax
Spine
Upper extr.
Lower extr.
Whole surf. + mult.
Regions

1
34
Urban
34/34 = 100%
30 mile/h
27/34 = 79%

2
33
Rural
33/33 = 100%
60 mile/h
20/33 = 61%

14.7%
26.5%
2.9%
5.9%
44.1%
5.9%

9.1%
30.3%
0
18.2%
24.2%
18.2%

Total
11.9%
28.4%
1.5%
11.9%
34.3%
11.9%

Injury factors
There is a clear high proportion of injuries to the upper extremities for motorcyclists,
followed by those to the chest (see Table 22 and Figure 25).
There was not a significant association between cluster membership and body region
(2=.345, df=1, p=.557). Both of the scenarios, urban and rural, show chest and
lower extremity injury type as being common. Lower extremity type is the most
frequent in urban areas (44.1%) followed by the chest (26.5%). For rural areas, the
chest proportion is 30.3% and the lower extremity 24.2%. Both Upper extremity and
multiple injury types seem associated more with rural areas than with urban areas.
France, Rhône region
Crash characteristics
Of the 1429 severely injured PTW cases of the Rhône region, most of them appeared
to be male (92%; see Figure 18) and young (18 to 24 years of age; see Figure 19).
The most common crash opponent for motorcyclists are cars (51%) but also crashes
with no crash opponent are common (30%; see Figure 20). In the Rhône area, 91% of
the severely injured motorcyclist wear a helmet. Most crashes happen during daytime
(72%; see Figure 24).
Crash scenarios
A first TwoStep Cluster analysis of the PTW data and the nominal variables DayNight,
FirstCrashOpponentOpponent,
DemographyAgeGroup,
DemographyGender,
and
Helmet yields a 4 cluster solution with an AIC of 8831.963 and a Cluster Quality
labelled as Fair. In this solution the variable DayNight has a predictor importance of
1.0 while the predictor importances of the variables FirstCrashOpponentOpponent,
DemographyAgeGroup, DemographyGender, and Helmet are 0.62, 0.08, 0.57 and
0.60, respectively.
A second TwoStep Cluster analysis without the variable DemographyAgeGroup yields a
4 cluster solution with an AIC of 3476.346 and a Cluster Quality labelled as Good. In
this
solution
the
variables
DayNight,
FirstCrashOpponentOpponent,
DemographyGender.
The clusters that were found could best be described as (see Table 23):

October 2016

107

Study on Serious Road Traffic Injuries in the EU






Male motorcyclist wearing a helmet hit by a car during daylight (437 of 437
cases);
Male motorcyclist wearing a helmet crashing with no crash opponent during
daylight (262 of 416 cases);
Male motorcyclist wearing a helmet hit by a car during night time (180 of 328
cases);
Female motorcyclist not wearing a helmet hit by a car during daylight (3 of 248
cases).

Injury factors
In the lower part of the Table we have also added the frequencies in the cross-table of
variables injury type and cluster number. Since there is only one face injury in this
data set we added this to the Head injuries. The proportions in the lower half of the
Table are column proportions; adding them over injury type these proportions
therefore all add up to 100%. For the powered two-wheeler victims we first of all see
that injuries to the lower extremities are generally the most common type of injury
(38%), followed by injuries to the upper extremities (20%), injuries to the thorax
(16%), and then by injuries to multiple regions (11%; see also Figure 25).
The Chi-square test for the cross-table of injury type by cluster number is very
significant (Chi-square = 45.11, df = 18, p < 0.001), indicating that for PTWs there is
a significant relationship between the variables injury type and cluster number. When
inspecting the injury types in the four separate clusters, we see the following
differences:
 Injuries to the head and face are relatively larger in daytime PTW crashes of
females who are hit by a car;

October 2016

108

Study on Serious Road Traffic Injuries in the EU




Upper extremities are relatively larger in males crashing without a crash
opponent by daytime;
Thorax, abdomen and pelvic spine content injuries are more common in males
crashing.

Germany
Crash characteristics
The 173 cases of severely injured motorcyclists showed that 96% of them are male
(Figure 18) and the age profile shows two peaks, one for the young drivers and one
for the middle aged centred around 50 (Figure 19). The majority of casualties (95%)
were the driver of the motorcycle.
In 43% of the crashes in which motorcyclists get severely injured the first crash
opponent is a car. About 51% of the crashes are either single without an opponent
(e.g. hitting the road) or are a crash with a (non-) fixed object. Vans or heavy vehicles
are other common crash opponents (see Figure 20). In one third of the cases are
single vehicle crashes, in nearly two third of the cases one other road user is involved.
Nearly 60% of the severely injured motorcyclists get in injured in a head-on collision
followed by single vehicle crashes and side impact.
Half of the motorcyclists crashes occur in urban areas, but also crashes on rural roads
are very common (43%; Figure 21). Two third of the motor rider crashes occur on
road sections (Figure 22) and in 88% of the cases the road was dry.
The majority of severe motorcyclist crashes occur in the motorcycle season (April to
September; see Figure 23), mostly by daylight (80%), especially in the early and later
afternoon (00:00 PM till 6:00 PM; Figure 24).
Crash scenarios
A first TwoStep Cluster analysis of the motorcyclist data with 173 cases and the above
stated variables yields a 3 cluster solution with an AIC of 7079 and a cluster quality
labelled as poor (0.1).
A second TwoStep Cluster analysis with the variables CrashOpponent, CrashType
Manoeuvre, JunctionType, CrashFactor, ActiveRoadUsers yields a 2 cluster solution
with an AIC of 1940 and a cluster quality labelled as fair (0.4).
A third TwoStep Cluster analysis yields a 2 cluster solution with an AIC of 783 and a
cluster quality labelled still as good ( 0.5) with the variables (predictor importance)
ActiveRoadUsers (1.0), CrashOpponent (0.24), and CrashFactor (0.23). The clusters
can be summarised as follows (see Table 24):
 Motorcycle in a single vehicle crash while speeding (55 of 112).
 Motorcycle hit by a car in a situation with inadequate information acquisition
(25 of 61).
Injury factors
Table 24 shows the most common crash scenarios including the frequencies of the AIS
body region injury type per scenario. For the motorcycle casualties the injuries of the
lower extremities are most common (45%) followed by thorax injuries (29%) and
multiple region injuries and head injuries (each 9%; see also Figure 25).
The Chi-square test for the cross table of injury type by scenario is not significant
(Chi-square=5.939 df=5, p<0.312), indicating that for motorcyclists there is no
significant relationship between the injury type and the crash scenario.

October 2016

109

Study on Serious Road Traffic Injuries in the EU

Having a closer look into the injury distribution within the two scenarios, there are
indications that:
 The single vehicle crashes show a higher share of thorax injuries compared to
the crashes with another participant involved. However, the share of injuries of
the lower extremities is lower;
 Abdomen and pelvic injuries are more common for single motorcyclist crashes.
Table 24: Crash scenarios and injured body regions for the German motorcyclist data (GIDAS).
Cluster nr.

2

1

N

112

61

Active Road Users

2 road users
110/112
=98.2%

Single crash
51/61
=83.6%

CrashOpponent

car
67/112
=59.8%

No crash opponent
39/61
=63.9%

Crash Factor

Inadequate
acquisition
86/112
=76.8%

information

Speeding
41/61
=67.2%

Injury Type

Total

Head, Face, Neck

11/112
=9.8%

5/61
=8.2%

16/173= 9.2%

Thorax

31/112
=27.7%

19/61
=31.1%

50/173= 28.9%

Abdomen and pelvic
cont.s

1/112
=0.9%

4/61
=6.6%

5/173 = 2.9%

Spine

3/112
=2.7%

3/61
=4.9%

6/173= 3.5%

Upper extr.

2/112
=1.8%

0/61
=0%

2/173= 1.2%

Lower extr.

52/112
=46.4%

25/61
=32.5%

77/173 = 44.5%

Whole surf. + mult.
Regions

12/112
=10.7%

5/61
=8.2%

17/173 = 9.8%

For the 173 motorcycle casualties 1068 single injuries (mean of 6 injuries per
casualty) were recorded of which 287 injuries (mean of 2 severe injuries per casualty)
have a severity of AIS08=3 or larger. The majority of fractures were caused by the
contact with the opponent (41/142=28.9%) or the contact with the road and
environmental features (e.g. the curb) (72/142=50.7%). These causes are also the
main causes for all injury types, that is 160/287=55.7% of all injuries were caused by
hitting the road, 76/287=26.5% were caused by the impact to opponent and
18/287=6.3% of the injuries were caused by contact with the own motorcycle.

October 2016

110

Study on Serious Road Traffic Injuries in the EU

Netherlands
Crash characteristics
The 2,365 cases of severely injured motorcyclists showed that more than 90% of
them is male (Figure 18) and mostly aged between 22 and 50 years of age (Figure
19).
Half of the crashes in which motorcyclists get
about one third is a single vehicle crash. Vans
crash opponents (see Figure 20). In two third
involved but also more than one other road user

severely injured are with a car and
or heavy vehicles are other common
of the cases, one other road user is
is common.

Most motorcyclists get severely injured in a head-on collision (42%) or a side-impact
collision (34%). The majority of the motorcyclists crashes occur on rural roads (45%),
but also crashes on urban roads are very common (40%; Figure 21). Nearly 60% of
the motor rider crashes occur on road sections and 40% at junctions (Figure 22) and
in nearly 90% of the cases the road is not wet or slippery.
The majority of severe motorcyclist crashes occur in the warmer months April to
September (Figure 23), mostly by daylight, especially in the early and later afternoon
(00:00 PM - 6:00 PM; Figure 24).
Crash scenarios
A first TwoStep Cluster analysis of the moped data with 2365 cases from the linked
BRON-DHD data and the nominal variables Month, Time, opponent, CrashType,
Gender, RoadType, Junction, Surface, and SpeedLimit and the interval variables
ActiveRoadUsers and Age yields a 5 cluster solution with an AIC of 51928.339 and a
cluster quality labelled as Poor. In this solution the variables Month, Time, Gender,
Age, Surface and Junction have a predictor importance lower than 0.7.
A second TwoStep Cluster analysis without the latter five variables yields a 3 cluster
solution with an AIC of 21241.282 and a cluster quality labelled as Fair. In this solution only
the variables opponent, CrashType, RoadType and SpeedLimit have a predictor importance
higher than 0.9; the predictor importance of ActiveRoadUers is lower than 0.3.
A third TwoStep Cluster analysis only applied to the four variables opponent,
CrashType, RoadType and SpeedLimit yields a 3 cluster solution with an AIC of
19987.028 and a cluster quality also labelled as Fair. In this solution all four
remaining variables have a predictor importance higher than 0.9. These 3 clusters are
described in Table 25 in order of cluster size and can best be summarised as follows:
 Side-impact crash during turning manoeuvre with another car as crash
opponent on an urban 50 km/h road (116 of 953 cases);
 Side-impact crash during turning manoeuvre with another car as crash
opponent on a rural 80 km/h road (154 of 714 cases);
 Collision with a fixed object on a rural 80 km/h road (127 of 698 cases).
Injury factors
In the lower part of the Table are the frequencies in the cross-table of variables injury
type and cluster number for this data set. For the motorcycle victims we first of all see
that the injuries to the lower extremities are generally the most common type of
injury (38%), followed by injuries to the thorax (24%), and then by injuries to the
head (16%; see also Figure 25).

October 2016

111

Study on Serious Road Traffic Injuries in the EU

The Chi-square test for the cross-table of injury type by cluster number is very
significant (Chi-square = 70.391, df = 12, p < 0.001), indicating that for motorcycles
there is a significant relationship between the variables injury type and cluster
number. When inspecting the injury types in the three separate clusters, we see that
the relation between injury type and cluster type for motorcycles is mainly due to the
fact that:
 injuries to the thorax are relatively larger in collisions with a fixed object;
 injuries to the lower extremities and injuries to multiple body regions are
relatively larger in motorcyclist that are hit by a car, both on rural 80 km/h and
urban 50 km/h roads.
Table 25: Crash scenarios and injured body regions for the Dutch motorcyclist data (BRON-DHD).
Cluster nr.
N
Crash type

Sweden
Crash characteristics
Within the STRADA data selected, there are 1,157 severely injured motorcyclists of
which the vast majority (91%) are male (Figure 18). There are a couple of peaks in
the age distribution, firstly in the age distribution for motorcyclists in their early 20’s
and then for those between the age of 45-55 (Figure 19). In respect of seating
position, 95% are the motorcycle driver, with the remaining 5% passenger.
Single vehicle impact is the most common crash scenario (43%). Impacts with a car
are next most common accounting for 40% of the crashes (Figure 20). Considering
the number of road users, 50% of cases with have a single road user. A further 46%
involve 2 road users.
Considering road type, 55% of the crashes occur in a rural environment, 39% in urban
areas and 7% on motorways (Figure 21). The most frequent speed limit is 50 km/h
(37%) followed by 70 km/h (34%). Looking at any junction layout, the data is
primarily distinguished by either ‘intersection’ or ‘road section’. Around two third
(65%) of crashes occur on a road section (no junction; Figure 22).

October 2016

112

Study on Serious Road Traffic Injuries in the EU

There is a clear rise in the number of motorcycle crashes across the summer months
(Figure 23). There is a rise in the proportion of crash occurring in the afternoon into
the early evening, with the greatest proportion being between 3 and 6 pm (Figure 24).
Crash scenarios
A first TwoStep cluster analysis was undertaken with the nominal variables Urban
Number_Road_Users Crash_opponent Location_junction Hour Month Role and Sex and
the continuous variables SpeedLimit and Age, a total of 10 input variables.
This resulted in a 2 cluster solution with an AIC of 20812.91 and a cluster quality
labelled as Fair. Only the variables Crash_opponent and Number_road_users have a
predictor importance > 0.5.
A second TwoStep Cluster analysis used input variables from the first with a predictor
importance > 0.5, Number_road_users and Crash_opponent. This produced another 2
cluster solution with an improved AIC of 2347.48 and a cluster quality labelled as
Good. In this solution the variable Number_road_users has importance 1.0 and
crash_opponent 0.58. The 2 clusters are described in Table 26 and can best be
summarised as:
 1 road user in a single vehicle crash* (451 of 621 cases);
 2 road users where the opponent was a car (393 of 536 cases).
Table 26: Crash scenarios and injured body regions for the Swedish motorcyclist data (STRADA).
Cluster nr.

2

1

N

621 (53.7)%

536 (46.3%)

Number Road Users

1 (92.9%)

2 (100%)

Crash Opponent

Single Vehicle (73.9%)

Car (73.3%)

Injury Type

Total

Head, Face, Neck

9.7%

9.7%

9.6%

Thorax

37.7%

27.8%

33.1%

5.2%

4.9%

5.0%

7.1%

3.0%

5.2%

Upper extr.

3.5%

6.9%

5.1%

Lower extr.

22.7%

33.6%

27.7%

14.2%

14.2%

14.1%

Abdomen
cont.s
Spine

and

Whole surf.
Regions

+

pelvic

mult.

Injury factors
The table of injury type by body region shows that the chest is the most common
injury type (38%), followed by the head (22%; see also Figure 25).
A chi-square test of association has been performed on the 2 x 7 contingency table
generated from cluster number by injury type (2=36.505, df =6, p<0.001) which
shows that an association exists between injury type and crash scenario as defined by
the 2 clusters. Visual inspection of the table shows the following most prominent
differences:

October 2016

113

Study on Serious Road Traffic Injuries in the EU




Injuries to the thorax are dominant in single vehicle crashes;
Injuries to the lower extremities are dominant in crashes where the
motorcyclist is hit by a car.

IGLAD database
Crash characteristics
The 49 cases of severely injured motorcyclists showed that 92%of them are male
(Figure 18) and mostly aged between 22 and 34 years of age (Figure 19). 94% of the
casualties were the rider of the vehicle. The motorcycles were mainly 5-10 years old.
In 61% of the crashes in which motorcyclists get severely injured the first crash
opponent is a car, in 29% of the cases it was a fixed object or no crash opponent
(Figure 20). However, in 80% of the cases one other participant was involved and in
14% of the cases, the crash included only the motorcyclist and no other active traffic
participants. Most crashes were preceded by a (U-) turning manoeuvre (63%) or by
going straight or rounding a curve (27%).
Half of the motorcyclists crashes occur in urban areas, but also crashes on rural roads
are very common (43%; Figure 21). In 90% of the cases the road is not wet or
slippery. In 63% of the cases inadequate information acquisition contributed to the
crash, but also speeding & red light running, as well as tailgaiting and wrong way
driving are common contributing factors.
The majority of severe motorcyclist crashes occur by daylight, especially in the early
and later afternoon (3:00 PM till 6:00 PM; Figure 24).
Crash scenarios
A first TwoStep Cluster analysis of the cyclist data with 49 motorcyclist cases and the
above stated variables yields a 4 cluster solution with an AIC of 1595 and a cluster
quality labelled as poor (0.2). The model suggests the removal of the variables Role,
Manoevre, VehicleAge.
A second TwoStep Cluster analysis of the cyclist data with CrashOpponent,
Manoeuvre, RoadType, ActiveRoadUsers, CrashFactor and AgeGroup yields a 3 cluster
solution with an AIC of 671 and a cluster quality labelled as poor (0.2).
A third TwoStep Cluster analysis yields a 4 cluster solution with an AIC of 342 and a
cluster quality labelled as good (0.6). The variables (predictor importances) are
RoadType (1.0), CrashFactor
(0.49), AcvtiveRoadUsers (0.38), and Manoeuvre
(0.27). The four clusters that have been found can be described according to the
following major characteristics (see Table 27):
 Motorcyclist crashes with another traffic participant on an urban road in a
situation with inadequate information acquisition and preceded by a turning
manoeuvre (12 of 17).
 Motorcyclist crashes with another traffic participant on an rural road in a
situation with inadequate information acquisition and preceded by a turning
manoeuvre (9 of 17).
 Motorcyclist crashes with another traffic participant on an urban road while
speeding and going straight (1 of 11).
 Motorcyclist in a single vehicle crash on a motorway preceded by wrong way
driving and a turning manoeuvre (2 of 4).

October 2016

114

Study on Serious Road Traffic Injuries in the EU

Injury factors
Table 27 shows the frequencies of the MAIS3+ injured body regions per scenario. For
the motorcyclist casualties injuries of the lower extremities (45%) are most common
followed by thorax injuries (18%) and injuries to the head (16%; see also Figure 25).
The Chi-square test for the cross table of injury type by scenario is not significant
(Chi-square=20.205 df=15, p<0.164), indicating that for motorcyclists there is no
significant relationship between the injury type and the crash scenario. A closer look
into the injury distribution provides indications that in motorway crashes, particularly
head injuries and multiple injuries are common.
Table 27: Crash scenarios and injured body regions for the IGLAD motorcyclist data (IGLAD).
Cluster nr.

Figure 22: Road configuration where crashes occur in which motorcyclists (powered two-wheelers for
databases with *) get severely injured in the Czech Republic* (CziDAS data), England (linked: STATS19HES), England (in-depth: RAIDS/OTS), Germany (GIDAS data), the Netherlands (BRON-DHD), and Sweden
(STRADA). **A staggered junction is a junction were the side roads are not opposite to each other.

Figure 24: Time period of the day during which motorcyclists (powered two-wheelers for databases with *) get
severely injured in the Czech Republic* (CziDAS data), England (linked: STATS19-HES), France*, Rhône
region (Rhône trauma register data), Germany (GIDAS data), the Netherlands (BRON-DHD), Sweden
(STRADA) and the European sample from the IGLAD database*.

Car occupants
Czech Republic
Crash characteristics
The CzIDAS car occupant data include 64 seriously injured casualties. two third of the
severely injured car occupants were male (see Figure 26), particularly youngsters
(Figure 27) and two third were the driver of the car (see Figure 28).
In half of the cases a car was the first crash opponent, in 14% of the cases it was a
heavy vehicle and 38% of the cases were a crash with an object as first crash
opponent or no crash opponent (see Figure 29). In 22% of the cases only one road
user was involved, 34% were crashes with 2 road users and in 44% 3 or more road
user were involved.
The majority of car occupants was injured on rural roads (see Figure 30) with a speed
limit of 90 km/h (66%) and the crashes occurred mainly on road sections (72%; see
Figure 31) and in dry conditions (70%). In 92% of the cases the crashes occurred in
not physically divided two-way traffic and with no special location (89%).
Two third of the crashes were head-on collisions, 25% side impact collisions. 26% of
the car occupants got injured in an crash that was characterized by the manoeuvre of
going ahead round curve (39% going ahead other) and 30% got injured in a (U-)
turning crash.
69% of the crashes happened during the day, most of them in the forenoon and the
early afternoon during 6am-3 pm (see Figure 33).
Crash scenarios
A first TwoStep Cluster analysis of the car occupant data with 64 cases and the above
stated variables yields a 3 cluster solution with an AIC of 2324 and a cluster quality
labelled as poor. The next iteration was done with the variables Month Time,,
CrashOpponent, ActiveRoadUsers, Manoeuvre, RoadType, CarriageWay, SpeedLimit,
JunctionType, and CrashFactor.
The second round yields a 4 cluster solution with an AIC of 1603 and a cluster quality
labelled as fair.
The third round yields a 4 cluster solution with an AIC of 452 and a cluster quality
labelled as fair. The variables (predictor importances) are ActiveRoadUsers (1.0),
CrashOpponent (0.93), Manoeuvre (0.64) and JunctionType (0.62). The results of this
analysis (see Table 28) can best be summarised as:
 Car crashes with fixed object while going rounding a curve at a road section (6
of 23);
 Car crashes with another car and one other road user while going straight at a
road section (7 of 19);
 Car crashes with another car and two other road user while turning at a
crossroad (5 of 13);
 Car crashes with a truck and another road user while turning at a road section
(3 of 9).

Injury factors
The most prominent injuries in the CzIDAs car occupant data are thorax injuries
(31%) followed by injuries of the lower extremities (27%; see also Figure 34).
The Chi-square test for the cross table of injury type by scenario is not significant
(Chi-square =19.382, df =18, p<0.369), indicating that for car occupants there is no
significant relationship between the injury type and the crash scenario. Looking closer
at the data, the following indications can be found:
 Crashes with a truck lead mainly to injuries of the lower extremities and less to
the head than in other scenarios;
 Multiple region injuries most common in crashes with a fixed object, here also
abdomen, pelvic injuries and injuries of the spine are found (not in the other
scenarios);
 Upper extremities are injured only in turning accidents.
England
Crash characteristics
STATS19-HES - The STATS19 HES linked dataset comprises 9,413 severely injured car
occupants. Considering the gender just over two third of them are male slightly under
one third female (Figure 26). Adolescents (18 to 24 years) appear to be the most
dominant age group among MAIS3+ injured car occupants (Figure 27) with the mean

October 2016

125

Study on Serious Road Traffic Injuries in the EU

age being 35 (SD 20). In respect of seating position, 70% are drivers (see Figure 28),
18% front seat passengers and 12% seated in the rear.
The most common crash opponent is another car (45%; Figure 29). Combining
impacts with a fixed object (35%) and those with no impact partner (8%) shows
impacts with no road user opponent to comprise 43% of the total (Figure 29). These
results are supported when considering the number of active road users in the crash
where almost half involve 2 road users and a third only involve the car. This data set
records the location of the impact on the car; the majority of crashes resulting in
MAIS3+ injury have an impact to the front of the vehicle (66%) followed by the side
(29%). In 83% of the cases, the car is moving forward without turning or overtaking.
Road type (Urban/Rural/Motorway) has been derived from the road classification and
speed limit in this dataset and therefore the distribution is approximate. The indication
is that over half (56%) of the crashes with MAIS3+ car occupants occur in an rural
environment, 40% in urban areas and just 5% on motorways (Figure 30). Looking at
any junction layout, a third of the crashes occur on a road section (no junction). Of
the remaining, crashes at T/Y or staggered junctions are most prevalent (Figure 31).
The road surface was dry in 56% of cases.
Considering the date and time of the crash, the prevalence is highest in the winter
months, October to December (Figure 32) and there is a higher frequency of crashes
from mid-afternoon though till Midnight (Figure 33).
The most common crash factors that have been found in the STATS19-HES are;
 Loss of control (40%)
 Speeding and/or inappropriate speed (35%)
 Careless / reckless behaviour (23%)
 Driver under the influence (drugs/alcohol) (18%)
 Failed to look properly (17%)
 Road condition (wet/icy/poor surface; 14%).
RAIDS-OTS - The combined RAIDS/OTS dataset comprises 148 severely injured car
occupants of which 63% are male and 37% female (Figure 26). Age data is only
available for the RAIDS data (n=30); in this sample there are more occupants under
the age of 35 than over. In respect of seating position, 63% are drivers (see Figure
28), 21% front seat passengers and 16% seated in the rear.
The most common crash opponent according to this dataset is another car (45%).
However, combining impacts with a fixed object (35%) and those with no impact
partner (4%) shows impacts with no road user opponent to comprise just a slightly
smaller proportion (Figure 29). The number of road users shows 39% of cases with
just the single car involved. A further 46% involve 2 road users. Considering the
location of the impact on the car; the majority of crashes resulting in MAIS3+ injury
have an impact to the front of the vehicle (56%) followed by the side (38%). In 86%
of the cases, the car is moving forward without turning or overtaking.
Considering Road type (Urban/Rural/Motorway) the indication is that over half (53%)
of the crashes with MAIS3+ car occupants occur in a rural environment, a third in
urban areas and 14% on motorways (Figure 30). Around 60% of crashes occur where
the speed limit is 60mile/h or greater, a further 25% are where the limit is 30mile/h
or lower. Looking at any junction layout, a 70% of the crashes occur on a road section
(no junction). Of the remaining, crashes at T/Y or staggered junctions are most
prevalent, 18%, and 8% are at cross roads. The road surface was dry in 55% of cases
(Figure 31).

October 2016

126

Study on Serious Road Traffic Injuries in the EU

Month and time are not available in English in-depth data for privacy protection and so
daytime/night-time has been used as a substitute. 60% of the severe injury crashes
occur during the daytime, with 40% at night time.
The RAIDS/OTS data gave also some starting points to say somewhat more about the
causes of crashes. The factors that were found to be most common in crashes where
car occupants get severely injured are:
 Loss of control (58%);
 Speed – either in excess of speed limit or too fast for the conditions (56%);
 Careless / reckless behaviour (49%).
Other contributing factors that were found: drivers in the crash were distracted in
16% of cases and under the influence of drugs or alcohol in 17% of cases. Failure to
look properly (17%), failure to judge another road users path or speed (15%) and
aggressive driving (16%) are other common factors. Road design was mentioned in
almost 12% of cases.
Crash scenarios
STATS19-HES - A first TwoStep Cluster analysis of the car data with 9413 cases in the
STATS19-HES database was undertaken using the nominal variables CasualtyRole
Month, Time, opponent, CrashType, Gender, Junction, Surface, Manoeuvre
ActiveRoadUsers and SpeedLimit and the interval variable Age, a total of 12 input
variables. This resulted in a 2 cluster solution with an AIC of 260780.4 and a cluster
quality labelled as Poor.
A second TwoStep Cluster analysis used input variables from the first with a predictor
importance > 0.5, opponent, ActiveRoadUsers, Time and Manoeuvre. Again a 2 cluster
solution was returned with an AIC of 105134.6 and a cluster quality labelled as Fair. In
this solution the variables opponent, ActiveRoadUsers and Time have a predictor
importance > 1.0.
A third TwoStep Cluster analysis only using the latter three variables yields a 2 cluster
solution with an AIC of 83239.131 and a cluster quality labelled as Good. In this
solution Opponent and ActiverRoadUsers have a predictor importance of 1.0 and Time
has 0.61. The 2 clusters are described in Table 29 in order of cluster size and can best
be summarised as:
 car to car crashes, with two active road users (3259/6021) most frequently
occurring during the rush hour period (5-6pm; 245 of 6021 cases);
 Single vehicle crashes in to a fixed object (2995/3392) occurring most
frequently at night (11pm to midnight; 256 of 3392 cases).
Table 29: Crash scenarios for England car occupant data (STATS19-HES)
Cluster nr.
N
Opponent
ActiveRoadUsers
Time

1
6021
Car
(69.9%)
2
(76.1%)
5:00 PM
(7.5%)

2
3392
Fixed Object
(87.5%)
1
(99.6%)
11:00 PM
(8.5%)

Loss of control and speeding/inappropriate speed remain the most commonly reported
factors in both clusters, however there is evidence for a higher proportion of crashes
with these factors when the car impacts with a fixed object. Alcohol or drugs feature in
car to fixed object crashes, but not so much in car to car impacts, whereas failure to
look and/or judge another road users path or speed are more associated with car to

October 2016

127

Study on Serious Road Traffic Injuries in the EU

car impacts. The road condition is also reported more in car to fixed object crashes
than for car to car impacts.
RAIDS/OTS - Also a cluster analysis of the RAIDS/OTS data split the data into two
common scenarios based upon two input variables CrashOpponent and
ActiveRoadUsers. The first (62% of severe car occupant casualties) is defined by car
to car crashes, with two active road users. The second (38% of casualties) comprises
single vehicle crashes in to a fixed object. These two clusters support the picture
produced for the national English data which shows very similar distributions of these
two variables.
A first TwoStep Cluster analysis of the car data with 148 cases was undertaken using
the nominal variables CasulaltyRole DayNight, CrashOpponent, CrashType, Gender,
Junction, Surface, Manoeuvre ActiveRoadUsers, area and SpeedLimit a total of 11
input variables. Age was not included due to non-availability in OTS. This resulted in a
2 cluster solution with an AIC of 2784.06 and a cluster quality labelled as Poor.
A second TwoStep Cluster analysis used input variables from the first with a predictor
importance > 0.5, ActiveRoadUsers and CrashOpponent. Again a 2 cluster solution
was returned this time with an improved AIC of 350.66 and a cluster quality labelled
as Good. In this solution the variable opponent has importance 0.7 and opponent 1,0.
The 2 clusters are described in Table 30 and can best be summarised as:
 Car to car crash with two active road users involved (46 of 91 cases);
 Car crash with a fixed object and only one active road user involved (48 of 57
cases).
Whilst loss of control and speed are the most frequent for both clusters, they occur
more frequently in crash scenarios where a car hits a fixed object than in car to car
crashes. The car to fixed object crashes feature driver under the influence and poor
manoeuvre, whereas those car to car crashes cluster features errors of judgement
such as failing to look properly and misjudging the path or speed of another vehicle.
Table 30: Crash scenarios and injured body regions for England car occupant data (RIADS & OTS).
Cluster nr.
N
Crash opponent
Number of active road
users
Injury type
Head, Face, Neck
Thorax
Abdomen and pelvic
cont.s
Spine
Upper extr.
Lower extr.
Whole surface+
Mult. Regions

1
91
Car
64/91 = 70%
Two road users
68/91 = 75%

2
57
Fixed object
48/57 = 84%
One road user
57/57 = 100%

17.6%
26.4%
3.3%

21.1%
28.1%
0%

Total
18.9%
27.0%
2.0%

4.4%
9.9%
23.1%
15.4%

10.5%
5.3%
14.0%
21.1%

6.8%
8.1%
19.6%
17.6%

Injury factors
The Table shows the injury distribution by body region for MAIS3+ car occupants in
the OTS/RAIDS data. Firstly, considering injuries of all severities, the chest is most
often injured (60%), followed by the lower extremity (59%) and the head (55%).
When just severe injury outcome is considered for each body region, the chest has the
highest proportion (43%) followed by arms (28%) and the head (24%; see also Figure

October 2016

128

Study on Serious Road Traffic Injuries in the EU

34). About 11% of the severely injured car occupants did not wear a seat-belt, but
seat-belt wearing is unknown for 45%.
A chi-square test of association has been performed on the 2 x 7 contingency table
generated from cluster number by injury type (2=7.118, df =6, p=.301) however
some cells in the table have expected count < 5 and so the test is not valid. A further
chi-square test to test for an association between MAIS and cluster membership
(2=1.338, df =2, p=.512) was also not valid.
Looking in more detail at the injury type within each cluster, chest injury type is the
most common in both scenarios, (26.4% car to car, 2 road users and 28.1% Car to
fixed object, one road user). Injury type head, Spine and multiple have higher
proportions in the car to fixed object scenario, with extremities, both upper and lower
having higher proportions in the car to car scenario.
France, Rhône region
Crash characteristics
From the French Rhône region, data of 781 severely injured car occupant showed that
about two third of them is male (see Figure 26). Adolescents (18 to 24 years) appear
to be the most dominant age group among MAIS3+ injured car occupants (Figure 27).
According to the Rhône database, 80% of the severely injured car occupants has used
a seat belt but in only 30% of the crashes, an airbag was used.
Crashes with another car are most common (42%), followed by single vehicle crashes
and crashes with a fixed object (Figure 29). Somewhat more car crashes happen
during daytime than during the night (Figure 33).
Crash scenarios
A first TwoStep Cluster analysis of the completed car data with 781 cases and the
nominal variables DayNight, FirstCrashOpponentOpponent, DemographyAgeGroup,
DemographyGender, Seatbelt use and Airbag use yields a 2 cluster solution with an
AIC of 8227.363 and a Cluster Quality labelled as Fair. In this solution the variable
DemographyGender has a predictor importance of 1.0 while the predictor importances
of the remaining variables are all almost zero. This means that the variable
DemographyGender completely dominates the cluster solution, swamping out all the
other variables.
A second TwoStep Cluster analysis without the variable DemographyGender yields a 2
cluster solution with an AIC of 7041.746 and a Cluster Quality also labelled as Fair.
This solution is dominated by the variable DayNight which has a predictor importance
of 1.0 while the predictor importances of the other variables are all smaller than 0.30.
A third TwoStep Cluster analysis only applied to the FirstCrashOpponentOpponent,
DemographyAgeGroup, Seatbelt use and Airbag use results in a 4 cluster solution with
an AIC of 5122.018 and a Cluster Quality also labelled as Fair. In this solution the
variables FirstCrashOpponentOpponent, DemographyAgeGroup, Seatbelt use and
Airbag use have predictor importances of 0.50, 0.06, 1.00 and 0.95, respectively.
Finally removing variable DemographyAgeGroup from the analysis we obtain a 4
cluster solution with an AIC of 1820.769 and a Cluster Quality labelled as Good. In this
solution the variables FirstCrashOpponentOpponent, Seatbelt use and Airbag use have
predictor importances of 0.54, 1.00 and 0.88, respectively.
The following clusters were found and between brackets the number of cases that
exactly have the combination of characteristics described (Table 31):

October 2016

129

Study on Serious Road Traffic Injuries in the EU







Car crashes with no crash opponent in a crash without an airbag used but the
driver wearing a seat-belt (82 of the 231 cases; 59 cases are against fixed
objects);
Car with airbag hit by a another car while the driver is wearing a seatbelt (70
of the 213 cases);
Car without an airbag hit by another car while the driver is wearing a seatbelt
(134 of the 186 cases);
Car crashes with no crash opponent or with a fixed object in a car with no
airbag and the driver not wearing a seatbelt (30 of the 151 cases with no crash
opponent, 31 cases with fixed object).

Injury factors
In the lower part of the Table we have also added the frequencies in the cross-table of
variables injury type and cluster number. Since there is only one face injury in this
data set we added this case to the Head injuries. The proportions in the lower half of
Table 4 are column proportions; adding them over injury type these proportions
therefore all add up to 100%. For the car victims we first of all see that injuries to the
thorax are generally the most frequently in car occupants (27), followed by injuries to
the head and face (18%), injuries to the upper extremities (16%), and then by
injuries to multiple regions (14%) and to the lower extremities (14%; see also Figure
34).
The Chi-square test for the cross-table of injury type by cluster number is very
significant (Chi-square = 44.14, df = 18, p < 0.001), indicating that for cars there is a
significant relationship between the variable injury type and the clusters obtained in
the TwoStep Cluster analysis. Inspecting the distribution of the injury types over the
four separate clusters, we see the following most important differences:

October 2016

130

Study on Serious Road Traffic Injuries in the EU







Injuries to the head and face occur more often in crashes without an opponent
and no use of a seatbelt and airbag;
Injuries to the thorax occur more often in crashes with other cars when a
seatbelt but no airbag is used;
The Abdomen and pelvic contents are more often injured in single vehicle
crashes;
The number of injuries to the spine in crashes without an opponent using a
seatbelt but not an airbag is smaller than in the other three clusters;
The number of injuries to the upper extremities in crashes without an opponent
and with neither seatbelt nor airbag use is smaller than in the other three
clusters.

Germany
Crash characteristics
The 309 cases of severely injured car occupants in GIDAS showed that about two third
of them is male and one third female (Figure 26). Younger drivers (18 to 24 years)
appear to be the most dominant age group among MAIS3+ injured car occupants
(Figure 27). Nearly two third of the car occupants that get severely injured is involved
as a driver and nearly one third as a passenger (see Figure 28).
Crashes without any other road user are most common (50%), followed by cars as the
second most important crash opponent (34%; Figure 29). Also car crashes with a van
or heavy vehicle are common. About 40% of the car crashes are single crashes and
50% involve one other active road user. Furthermore, nearly two third of the MAIS3+
car crashes is a head-on crash, with side-impact collisions as important second crash
type. 47% of the crashes are characterized by the manoeuvre of going ahead other,
24% going ahead round curve and 18% of the car occupants got injured in a turning
crash.
Somewhat more than half of the crashes in which car occupants get severely injured
are on rural roads and about one third on urban roads (see Figure 30). About 80% of
the crash occur on road sections and 20% on junctions (Figure 31). In about two third
of the cases, the road is dry; in the other one third of the cases, the road was wet or
slippery. 75% of the crashes occur in not physically divided two-way traffic, 24% on
physically divided roadways. For the majority of cases (89%) the crash happened at
no special location. Concerning crash factors in nearly equal shares inadequate
information acquisition or speeding contributed to the crash. In 10% a fatigued driver
(casualty or opponent) caused the crash.
Most car occupants get severely injured in Winter (November- February) or in the
summer months July and August (Figure 32). Most injuries occur in the middle of the
day noon to 6:00 PM (Figure 33).
Crash scenarios
A first TwoStep Cluster analysis of the car occupant data with 309 cases and the
variables as stated above leaving out yields a 4 cluster solution with an AIC of 12 110
and a cluster quality labelled as poor (0.1). In this solution only the variables
Manoeuvre, RoadType, JunctionType, CarriageWay, Crash opponent, CrashFactor,
CrashType, ActiveRoadUsers and Location have a predictor importance larger than
0.2.
A second TwoStep Cluster analysis with the left variables yields a 4 cluster solution
with an AIC of 3840 and a cluster quality labelled as just fair (0.2).

October 2016

131

Study on Serious Road Traffic Injuries in the EU

A third TwoStep Cluster analysis different combinations of the former variables were
tested and the best solution with a 5 cluster solution with an AIC of 874 and a cluster
quality labelled as good (0.6) was achieved. In this solution the variables (predictor
importances) are RoadType (1.0), Manoeuvre (0.82), JunctionType (0.53) and
CarriageWay (0.34). The most common clusters are (see Table 32):
 Cars going ahead on rural road sections where the traffic is not physically
divided (71 of the 71 cases);
 Cars going ahead round a curve on rural road sections where the traffic is not
physically divided (53 of the 65 cases);
 Cars going ahead on an urban road sections where the traffic is not physically
divided (28 of the 65 cases);
 Cars in a turning manoeuvre on an urban crossroads where the traffic is not
physically divided (29 of the 63 cases);
 Cars going ahead on motorways, which have physical divided road directions
(28 of the 45 cases).
Table 32: Crash scenarios and injured body regions for the German car occupant data (GIDAS).
Cluster nr.

Injury factors
Table 32 shows the most common crash scenarios including the frequencies of the
MAIS3+ injured body regions per scenario. For the car occupant casualties the thorax
injuries are most common (39%) followed by injuries of the lower extremities (18%),
the head (16%) and multiple regions (14%; see also Figure 34).
The Chi-square test for the cross table of injury type by scenario is not significant
(Chi-square=33.825 df=24, p<0.088), indicating that for car occupants there is no
significant relationship between the injury type and the crash scenario. However, a
closer look into the injury distribution reveals a tendency that crashes on motorways
show a lowest share in injuries of the thorax and the lower extremities but the highest
share in head injurie, injuries of the abdomen and multiple region injuries. Crashes on
rural roads show the highest share of young drivers: 40.8% and 33.8%, respectively
and also the highest share of single crashes (38.5%) or crashes with a fixed object
(40.8%).
For the 309 car occupant casualties 1931 single injuries were recorded (mean of about
6 injuries per casualty) of which 574 injuries (mean of 2 injuries per casualty) have a
severity of AIS08=3 or larger. The majority of fractures were caused by the contact
with the internal of the own car. 15% of the injuries were inflicted by the seatbelt,
mainly causing rip fractures and organ injuries. 4% of the injuries were caused by a
whiplash, 11% by contact with the control panel and 15% by contact with the steering
wheel.
Netherlands
Crash characteristics
Data of 7,438 severely injured car occupants revealed that about two third of them is
male and one third female (Figure 26). Adolescents (18 to 24 years) appear to be the
dominant age group among MAIS3+ injured car occupants (Figure 27). Nearly ¾ of
the car occupants that get severely injured is involved as a driver and nearly ¼ as a
passenger (see Figure 28).
Crashes without any other road user are most common (44%), followed by cars as the
second most important crash opponent (Figure 29). Also car crashes with a van or
heavy vehicle are common in MAIS3+ car crashes. About one third of the car crashes
involve on other active road user, but also two or more other active road users are
common. Furthermore, nearly two third of the MAIS3+ car crashes are head-on
crashes, with side-impact collisions as important second crash type.
Somewhat more than half of the crashes in which car occupants get severely injured
are on rural roads, more than ¼ on urban roads and one fifth on motorways (see
Figure 30). About 70% of the crashes occur on road sections and 30% on junctions
(Figure 31). In two third of the cases, the road is dry; in the other one third of the
cases, the road was wet or slippery.
Most car occupants get severely injured in January-February and May-June (Figure
32). Most injuries occur in the afternoon, but also in the evening and morning (6:00
AM to 9:00 AM) are common (Figure 33).
Crash scenarios
A first TwoStep Cluster analysis of the car data from BRON-DHD with 7,438 cases and
the nominal variables Month, Time, opponent, CrashType, Gender, RoadType,
Junction, Surface, and SpeedLimit and the interval variables ActiveRoadUsers and Age
yields a 4 cluster solution with an AIC of 176738.016 and a cluster quality labelled as

October 2016

133

Study on Serious Road Traffic Injuries in the EU

Poor. Only the variables opponent, CrashType, RoadType, Junction, and SpeedLimit
have a predictor importance of 1.0.
A second TwoStep Cluster analysis only using the latter five variables yields a 3 cluster
solution with an AIC of 70880.198 and a cluster quality labelled as Fair. In this
solution only the variables opponent, CrashType, RoadType, and SpeedLimit have a
predictor importance of 1.0.
A third TwoStep Cluster analysis only using the latter four variables yields a 6 cluster
solution with an AIC of 44,875.352 and a cluster quality labelled as Fair. In this
solution all four variables have a predictor importance of 1.0. These 6 clusters are
described in Table 33 in order of cluster size and between brackets the number of
crash scenarios that exactly fit to the combination of characteristics that is the most
common in each of the clusters.
Table 33: Crash scenarios and injured body regions for the Dutch car occupant data (BRON-DHD).
Cluster
nr.
N
Crash type

The combination of factors via TwoStep Cluster analysis revealed the following six
most common scenarios (see Table 33):

October 2016

134

Study on Serious Road Traffic Injuries in the EU








Side-impact crash during turning manoeuvre with another car as crash
opponent on an urban 50 km/h road (233 of the 1,666 cases);
Collision with a fixed object on a rural 80 km/h road (1,306 of the 1,621
cases);
Head-on collision with a car on a rural 80km/h road (444 of the 1,577 cases);
Rear-end collision with another car on a 120 km/h motorway (102 of the 1,047
cases);
Collision with a fixed object on a 120 km/h motorway (239 of the 775 cases);
Collision with a fixed object on an urban 50 km/h road (625 of the 752 cases).

Injury factors
Since there are only two face injuries and one neck injury in this data set we added
these to the Head injuries and because there are only eight Whole surface area
injuries in this data set we added them to the Multiple region injuries. For the car
victims we first of all see that head injuries are generally the most common type of
injury (29%), closely followed by injuries to the thorax (26%), and by injuries to the
lower extremities (26%; Table 33 and Figure 34).
The Chi-square test for the cross-table of injury type by cluster number is very
significant (Chi-square = 150.923, df = 30, p < 0.001), indicating that for cars there
is a significant relationship between injury type and cluster.
When inspecting the injury types in the six separate clusters, we see that the relation
between injury type and cluster type is mainly due to the fact that:
 Injuries to the lower extremities are relatively much larger in the cases in car
to car side impacts on a 50 km/h road (31.1%) while being much smaller in the
cases where a car hits a fixed object on a motorway (16.9%).
 Moreover, injuries to multiple body regions are relatively larger in crashes
where a car hits a fixed object on a 80 km/h road (8.9%) while being much
smaller in car side-impact crashes on 50 km/h roads (4.7%).
 Injuries to the spine are less common when a car hits a fixed object on 50
km/h or 80 km/h roads than in other crash types and are most common in
rear-end collisions on motorways.
Sweden
Crash characteristics
The dataset comprises 3,291 severely injured car occupants of which 65% are male
and 35% female (Figure 26). There is a peak in the age distribution for young adults
aged 18-25 (Figure 27). In respect of seating position, 70% are drivers (see Figure
28) with the remaining 30% passengers of whom 11% are confirmed in the front and
7% in the rear.
Car to car impacts are the single most common crash scenario (37%). Impacts either
with a single vehicle involved or into a fixed object account for 45% of the crashes
(Figure 29). This is supported by the number of road users which shows 46% of cases
with just the single car involved. A further 45% involve 2 road users.
Almost two thirds of the crashes occur in a rural environment, a quarter in urban
areas and 15% on Motorways (Figure 30). The most frequent speed limit is 70 km/h
and almost 80% of crashes occur at this speed limit or greater. Looking at any
junction layout, the data is primarily distinguished by either ‘intersection’ or ‘road
section’. Over 3.4 (77%) crashes occur on a road section (no junction; Figure 31).
The months with the highest proportion of crashes are July and November. On the
whole the latter 6 months of the year have more crashes per month than those

October 2016

135

Study on Serious Road Traffic Injuries in the EU

between January to June (Figure 32). There is a rise in the proportion of crash
occurring in the afternoon, with the greatest proportion being between 3 and 6 pm
(Figure 33).
Crash scenarios
A first TwoStep cluster analysis was undertaken with the nominal variables Urban
Number_Road_Users Crash_opponent Location_junction Hour Month Role and Sex and
the continuous variables SpeedLimit and Age, a total of 10 input variables.
This resulted in a 2 cluster solution with an AIC of 70807.98 and a cluster quality
labelled as Fair. Only the variables Crash_opponent and Number_road_users have a
predictor importance > 0.5.
A second TwoStep Cluster analysis used input variables from the first with a predictor
importance > 0.5, Number_road_users and Crash_opponent. This produced a 5
cluster solution with an improved AIC of 2328,62 and a cluster quality labelled as
Good. In this solution the variable crash_opponent has importance 1.0 and so does
Number_road_users. The 5 clusters are described in Table 34 and can be summarised
as follows:
 Car hit by another car (all of the 946 cases)
 Car in single vehicle crash19 (all of the 945 cases)
 Car hit by a heavy vehicle (367 of the 544 cases)
 Car that crashes with a fixed object (all of the 473 cases)
 Car hit by another car and with at least one other traffic participant involved
(192 of the 383 cases)
Table 34: Crash scenarios and injured body regions for the Swedish car occupant data (STRADA).
Cluster nr.
N
Crash Opponent

Injury factors
The Table shows the injury distribution by body region for MAIS3+ car occupants in
the STRADA data. Due to the low number of face and neck injuries, these are
combined with the head. Similarly, the whole surface is combined with multiple

19

defined as accidents with no crash opponent recorded but with a crash code indicating a single vehicle
accident.

October 2016

136

Study on Serious Road Traffic Injuries in the EU

regions (see Table 34 and Figure 34). The chest shows the highest proportion followed
by the head and then the lower extremity.
A chi-square test of association has been performed on the 5 x 7 contingency table
generated from cluster number by injury type (2=90.821, df =24, p<0.001) which
shows that an association exists between injury type and crash scenario as defined by
the 5 clusters. A visual examination of the cross-tabulation shows that:
 Chest injuries have the highest proportion across all scenarios (30-38%) with
the highest being in the scenario where a car is hit by another car.
 The most striking differences are for the head where the scenario of car hit by
another car results in a lower proportion of injury to the head with the highest
proportion when the impact is to a large vehicle and single vehicle crashes.
 Lower extremity injury is higher in the scenarios where a car is hit by at least
one other car or where the car hits a fixed object.
IGLAD database
Crash characteristics
The 113 cases of severely injured car occupants showed that about 60% of them is
male and 40% female (Figure 26). Most MAIS3+ injured car occupants are in the age
of 18 to 44 (Figure 27). Sixty percent of the car occupants that get severely injured
are involved as a driver (see Figure 28). The vehicle in which the victim was driving
was mainly 1 to 10 years old.
Crashes with a car as first crash opponent are most common (56%), followed by
single vehicle crashes and crashes with a fixed object (31%) and heavy vehicles
(13%; Figure 29). About 26% of the car crashes are single crashes and 65% involve
one other active road user. Furthermore, 77% of the MAIS3+ car crashes is a head-on
crash, with side-impact collisions as important second crash type (19%). The majority
of crashes are preceded by going straight or rounding a curve (78%). Also turning
crashes are common (17%). Important crash factors are inadequate information
acquisition
(32%),
speeding
(26%),
and
a
driver
being
under
influence/fatigued/medically impaired (13%) or wrong-way driving (12%).
Somewhat more than half of the crashes in which car occupants get severely injured
are on urban roads (see Figure 30). In about 65% of the cases, the road is dry. Most
injuries occur from noon to 6:00 PM (Figure 33).
Crash scenarios
A first TwoStep Cluster analysis of the car occupant data with 113 cases and the
above stated variables yields a 2 cluster solution with an AIC of 4987 and a cluster
quality labelled as poor (0.2).
A second TwoStep Cluster analysis of the car occupant data with Role, Time,
DayNight, CrashOpponent, Manoevre, RoadType, CrashFactor, AgeGroup,
ActiveRoadUsers yields a 3 cluster solution with an AIC of 2651 and a cluster quality
labelled as fair (0.2).
A third TwoStep Cluster analysis yields a 3 cluster solution with an AIC of 1252 and a
cluster quality labelled as good (0.5). The variables (predictor importances) are
AcvtiveRoadUsers (1.0), CrashOpponent (0.76), DayNight (0.71),Time (0.27) and
Manoeuvre (0.14). The three clusters that have been found can be described
according to the following major characteristics (see Table 35):
 Car occupant in a crash with another car during daytime while going straight
(18 of 54)

October 2016

137

Study on Serious Road Traffic Injuries in the EU




Single car crash hitting an object during daytime while driving round a bend (6
of 33).
Car occupant in a crash with another car during night time while rounding a
curve (7 of 26).

Injury factors
Table 35 shows the frequencies of the MAIS3+ injured body regions per scenario. For
the car occupant casualties injuries of the thorax (33%) are most common followed by
multiple injuries (26%; see also Figure 34).
The Chi-square test for the cross table of injury type by scenario is not significant
(Chi-square=5.587 df=12, p<0.935), indicating that for car occupants there is no
significant relationship between the injury type and the crash scenario.
Further analyses of the scenarios showed that in scenario 1, speeding (46%) and
inadequate information acquisition (33%) play an important role. In scenario 2
speeding (31%) and wrong way driving (31%) are important contributing factors and
in scenario 3, inadequate information acquisition (48%) is important.

Figure 31: Road configuration where crashes occur in which car occupants get severely injured in the Czech
Republic (CziDAS data), England (linked: STATS19-HES), and England (in-depth: RAIDS/OTS), Germany
(GIDAS data), the Netherlands (BRON-DHD) and Sweden (STRADA data). *A staggered junction is a junction
were the side roads are not opposite to each other.

Figure 33: Time period of the day during which car occupants get severely injured in the Czech Republic
(CziDAS data), England (linked: STATS19-HES), France, Rhône region (Rhône trauma register data),
Germany (GIDAS data), the Netherlands (BRON-DHD), Sweden (STRADA data), and the European sample
from the IGLAD database.

HOW TO OBTAIN EU PUBLICATIONS
Free publications:
• one copy:
via EU Bookshop (http://bookshop.europa.eu);
• more than one copy or posters/maps:
from the European Union’s representations (http://ec.europa.eu/represent_en.htm);
from the delegations in non-EU countries
(http://eeas.europa.eu/delegations/index_en.htm);
by contacting the Europe Direct service (http://europa.eu/europedirect/index_en.htm)
or calling 00 800 6 7 8 9 10 11 (freephone number from anywhere in the EU) (*).
(*) The information given is free, as are most calls (though some operators, phone boxes or hotels may
charge you).